Neurological prosthesis

ABSTRACT

A method improving or restoring neural function in a mammalian subject in need thereof, the method including: using an input receiver to record an input signal generated by a first set of nerve cells; using an a encoder unit including a set of encoders to generate a set of coded outputs in response to the input signal; using the encoded outputs to drive an output generator; and using an output generator to activate a second set of nerve cells wherein the second set of nerve cells is separated from the first set of nerve cells by impaired set of signaling cells. In some embodiments, the second set of nerve cells produces a response that is substantially the same as the response in an unimpaired subject.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Application No. 61/381,646 (filed on Sep. 10, 2010). The subject matter of this application is also related to U.S. Provisional Application Nos. 61/378,793 (filed on Aug. 31, 2010), 61/308,681 (filed on Feb. 26, 2010), 61/359,188 (filed on Jun. 28, 2010), 61/378,793 (filed on Aug. 31, 2010), and 61/382,280 (filed on Sep. 13, 2010), and International Patent Application Nos. PCT/US2011/26526 (filed Feb. 28, 2011) and PCT/US2011/026525 (filed Feb. 28, 2011). The contents of each of the forgoing applications are incorporated by reference in their entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with U.S. Government support under R01 EY12978 from National Institute of Health (NIH). The U.S. Government has certain rights in the invention.

FIELD

The present invention relates to methods and devices for restoring or improving function, such as nerve function, in a subject. In particular, the present invention relates to methods and devices for restoring or improving motor, auditory, or other function using a set of encoders that closely mimic the input/output transformation of nerve cells to produce near-normal, normal or even super-normal function in a subject.

BACKGROUND

A number of neurological disorders including, e.g., motor neuron disorders (e.g., damage from stroke, injury, diseases such as ALS and MS), psychiatric disorders, memory disorders, and auditory disorders involve thempairment of a set of nerve cells. In many cases, the malfunction of these cells prevents or degrades communication between healthy sets of cells.

Various neural prosthetics have been developed to bypass malfunctioning cells to restore communication between the healthy cells. However, in typical cases, the bypassed impaired cells do not simply operate as signal pass-throughs, but instead provide processing of signals. In cases where a neural prosthetic does not accurately mimic the processing of the bypassed cells, the subject will exhibit degraded function in comparison to an unimpaired subject.

Thus, there exists a need to develop a neural prosthesis that bypasses or “jumps” impaired signaling cells, while providing a close proxy of the processing of the bypassed impaired signaling cells (i.e., such that the input to output transfer function of the prosthesis is well matched to that which would have been exhibited by the bypassed signaling cells in an unimpaired subject).

SUMMARY

As described in PCT/US2011/026525 (filed Feb. 28, 2011) (henceforth the “Retinal Application”), the applicants have developed a method and device for restoring or improving vision, increasing visual acuity, or treating blindness or visual impairment, or activating retinal cells. The method includes capturing a stimulus, encoding the stimulus, transforming the code into transducer instructions at an interface, and transducing the instructions to retinal cells. The device includes a way to capture a stimulus, a processing device executing a set of encoders, an interface, and a set of transducers, where each transducer targets a single cell or a small number of cells; the set of transducers is referred to as a high resolution transducer. In one embodiment, each encoder executes a preprocessing step, a spatiotemporal transforming step as well as an output-generating step. The method can be used for a retinal prosthesis to generate representations for a broad range of stimuli, including artificial and natural stimuli.

The stimulus is converted or transformed into a proxy of normal retinal output, that is, a form of output the brain can readily interpret and make use of as a representation of an image. The conversion occurs on about the same time scale as that carried out by the normal or near-normal retina, i.e., the initial retinal ganglion cell response to a stimulus occurs in a time interval ranging from about 5-300 ms. The methods and devices described in the Retinal Application can help restore near-normal to normal vision, or can improve vision, including both grayscale vision and color vision, in a patient or affected mammal with any type of retinal degenerative disease where retinal ganglion cells (which may also be referred to herein as “ganglion cells”) remain intact.

The retina prosthesis, like the normal retina, is an image processor—it extracts essential information from the stimuli it receives, and reformats the information into patterns of action potentials the brain can understand. The patterns of action potentials produced by the normal retinal are in what is referred to as the retina's code or the ganglion cell's code. The retina prosthesis converts visual stimuli into this same code, or a close proxy of it, so that the damaged or degenerated retina can produce normal or near-normal output. Because the retina prosthesis uses the same code as the normal retina or a close proxy of it, the firing patterns of the ganglion cells in the damaged or degenerated retina, that is, their patterns of action potentials are the same, or substantially similar, to those produced by normal ganglion cells. A subject treated with such devices will have visual recognition ability closely matching the ability of a normal or near-normal subject.

The applicants have realized that this approach may be applied more generally to provide methods and devices for restoring or improving function, such as neurological, motor, or auditory function in a human patient or other mammalian subject. As in the retinal case, a device including a processor which implements a set of encoders is provided which receives an input signal and generates an output signal, such that the input/output transformation operates as a close proxy of the signal processing that would occur in a normal patient.

In some embodiments, the input signal comes from a first set of healthy cells (e.g., supplementary motor area neurons), and the output signal drives a response in second set of healthy cells (e.g., spinal motor neurons) that are separated from the first set by an impaired set of signaling cells (e.g., damaged primary motor cortex neurons). The encoders provide a close proxy of the processing that would occur in the set of signaling cells in an unimpaired subject, allowing the impaired cells to be bypassed or jumped while reducing or eliminating degradation in function.

In some embodiments, the input signal is an external stimulus (e.g., sound waves), which are detected by the device (e.g., using a microphone). The input signal is processed using a set of encoders to generate a coded output used to drive healthy cells (e.g., spiral ganglion cells in the inner ear) which are associated with an impaired set of signaling cells (e.g., cochlear hair cells used to detect sound in the inner ear). The encoders provide a close proxy of the processing that would occur in the set of signaling cells in an unimpaired subject, allowing the impaired cells to be bypassed or jumped over, reducing or eliminating degradation in function.

To ensure that the encoders provide a close proxy of the processing that would occur in the signaling cells of a normal subject, a strategy may be employed of using experimental data (e.g., collected in vivo or in vitro from unimpaired cells) to generate a model of the signaling cells' processing. Accordingly, a data-driven phenomenological model is provided, directly analogous to those developed to model retinal processing in the Retinal Application.

Because this approach leverages experimental data, the generated encoders can accurately simulate the signaling cell processing, without requiring a detailed abstract understanding of the signaling cells' underlying processing schemes. For example, it is believed that retinal processing in primates and humans highlights features in the visual stimulus useful for pattern recognition tasks (e.g., facial recognition) while de-emphasizing or eliminating other features (e.g., redundant information or noise) to allow for efficient processing in the brain. Similar processing occurs in many other types of cells or neural networks (e.g., spinal motor neurons or motor neuron networks or spiral ganglion cells in the ear, etc.). As of yet, there is no complete abstract understanding of the details of these natural processing schemes, which developed as the result natural selection over the course of eons. However, despite this lack of abstract understanding, the devices and techniques described herein can capture the benefit of this processing, by accurately mimicking the response of unimpaired cells

A method improving or restoring neural function in a mammalian subject in need thereof is disclosed, the method including: using an input receiver to record an input signal generated by a first set of nerve cells; using an a encoder unit including a set of encoders to generate a set of coded outputs in response to the input signal; using the encoded outputs to drive an output generator; and using an output generator to activate a second set of nerve cells where the second set of nerve cells is separated from the first set of nerve cells by impaired set of signaling cells; where the second set of nerve cells produces a response that is substantially the same as the response in an unimpaired subject.

In some embodiments, the first set of nerve cells includes supplementary motor area neurons; the second set of nerve cells includes spinal motor neurons; and the impaired set of signaling cells includes primary motor cortex neurons.

Some embodiments include generating the input signal as a time resolved series of values {right arrow over (a)} corresponding to the pattern of neural activity generated in the first set of nerve cells; and transforming the values {right arrow over (a)} to a time resolved series of output values {right arrow over (c)} by applying a transformation.

In some embodiments, {right arrow over (c)} is a vector valued function, where each element of the vector is a value corresponding to a firing rate of a single cell or small group of cells from the second set of nerve cells.

In some embodiments, {right arrow over (c)} is a vector valued function, where each element of the vector is a value corresponding to the total firing rate of second set of nerve cells.

In some embodiments, {right arrow over (c)} is a vector valued function, where each element of the vector is a value corresponding to the total firing rate of a respective subpopulation of the second set of nerve cells.

In some embodiments, the second set of nerve cells includes motor neurons, and each subpopulation innervates a different respective muscle.

In some embodiments, the transformation includes: a set of spatiotemporal linear filters; and a nonlinear function.

In some embodiments, the transformation is characterized by a set of parameters; and where the set of parameters corresponds to a result of fitting the transformation to experimental data obtained by: exposing an unimpaired subject to a broad range of reference stimuli; recording a first response in the unimpaired subject corresponding to the first set of nerve cells; recording a second response in the unimpaired subject corresponding to the second set of nerve cells.

In some embodiments, the second response includes the firing rate of individual nerve cells.

In some embodiments, the spatiotemporal filters are parameterized by a set of K weights.

In some embodiments, the method of claim 11, where K is in the range of 1-100 or any subrange thereof, e.g., in the range of 5-20.

In some embodiments, the nonlinear function is parameterized as a cubic spline function with M knots.

In some embodiments, M is in the range of 1-100 or any subrange thereof, e.g., in the range of 2-20.

In some embodiments, the spatiotemporal linear filters operate over P time bins, each having a duration Q.

In some embodiments, P is in the range of 1-100, or any subrange thereof, e.g., in the range of 5-20.

In some embodiments, Q is in the range of 10 ms-100 ms. In some embodiments, Q is in the range of 1 ms-1000 ms or any subrange thereof.

In some embodiments, the broad range of reference stimuli includes at least one chosen from the list consisting of: motion in an environment including one or more obstacles; manipulation of objects having different weights; and moving a cursor to one of several locations on a display.

In some embodiments, the second set of nerve cells are light sensitized; and the step of using an output generator to activate a second set of nerve cells includes: generating a time resolved optical signal; and directing the optical signal to the second set of nerve cells to stimulate a response.

Some embodiments include sensitizing the second set of nerve cells to light

In some embodiments, the optical signal includes a spatially and temporally modulated pattern of light.

In some embodiments, the modulated pattern of light includes an array of pixels having an average pixel size of less than 0.1 mm and a pixel modulation rate of greater than 100 Hz.

In some embodiments, the step of using an output generator to activate a second set of nerve cells includes: generating a set of electrical pulses; and directing the electrical pulses the second set of nerve cells to stimulate a response.

In another aspect, a device improving or restoring neural function in a mammalian subject in need thereof is disclosed, the device including: an input receiver configured to record an input signal generated by a first set of nerve cells; an output generator configured to activate a second set of nerve cells, where the second set of nerve cells is separated from the first set of nerve cells by an impaired set of signaling cells; and an encoder unit including a set of encoders that generate a set of coded outputs in response to the input signal, where the set of coded outputs control the output generator to activate the second set of nerve cells to produce a response to the input signal that is substantially the same as the response in an unimpaired subject.

In some embodiments, the input receiver includes an electrode.

In some embodiments, the input receiver includes an array of electrodes.

In some embodiments, the array of electrodes records the response of at least 100 neurons in the first set of neurons.

In some embodiments, the encoder unit includes at least one processor.

In some embodiments, the at least one processor includes a digital signal processor.

In some embodiments, the at least one processor includes multiple processors configured to operate in parallel.

In some embodiments, the output generator includes a set of electrodes.

In some embodiments, the output generator includes an optical signal generator. In some embodiments, the optical signal generator includes a digital light processor.

In some embodiments, the optical signal generator includes an array of light emitting diodes.

In another aspect, a non-transitory computer readable media is disclosed having computer-executable instruction including instruction for executing steps including: recording an input signal generated by a first set of nerve cells; using an a encoder unit including a set of set of encoders to generate a set of coded outputs in response to the input signal, and using the coded outputs to control an output generator to activate a second set of nerve cells where the second set of nerve cells is separated from the first set of neurons by an impaired set of signaling cells; where the second set of nerve cells produces a response to the input signal that is substantially the same as the response in an unimpaired subject.

In another embodiments, a method of improving or restoring auditory function in a mammalian subject in need thereof, is disclosed the method including: using an audio receiver to generate an input signal in response to an audio stimulus; using an a encoder unit including a set of set of encoders to generate a set of coded outputs in response to the input signal; using the encoded outputs to drive an output generator; and using an output generator to activate a set of auditory neurons, where the set of auditory neurons are associated with a set of impaired signaling cells; where the auditory neurons produce a response that is substantially the same as the response to the stimuli in an unimpaired subject.

In some embodiments, the set of auditory neurons include spiral ganglion cells; and the impaired set of signaling cells includes cochlear hair cells.

Some embodiments include generating the input signal as a time resolved series of values {right arrow over (a)} corresponding to the audio stimulus; transforming the values {right arrow over (a)} to a time resolved series of output values {right arrow over (c)} by applying a transformation.

In some embodiments, {right arrow over (c)} is a vector valued function, where each element of the vector is a value corresponding the firing rate of a single spiral ganglion cell or small group of spiral ganglion cells from the set of auditory neurons.

In some embodiments, {right arrow over (c)} is a vector valued function, where each element of the vector is a value corresponding to the total firing rate of a respective subpopulation of the auditory set of neurons.

In some embodiments, the transformation includes: a set of spatiotemporal linear filters; and a nonlinear function.

In some embodiments, the transformation is characterized by a set of parameters; and where the set of parameters corresponds to a result of fitting the transformation to experimental data obtained by: exposing an unimpaired subject to a broad range of reference audio stimuli; and recording a response in the unimpaired subject corresponding to the set of auditory neurons.

In some embodiments, the response includes the firing rate of individual neurons.

In some embodiments, the spatiotemporal filters are parameterized by a set of K weights.

In some embodiments, K is in the range of 1-100 or any subrange thereof, e.g., in the range of 5-20.

In some embodiments, the nonlinear function is parameterized as a cubic spline function with M knots.

In some embodiments, M is in the range of 1-100 or any subrange thereof, e.g., in the range of 2-20.

In some embodiments, the spatiotemporal linear filters operate over P time bins, each having a duration Q.

In some embodiments, P is in the range of 1-100 or any subrange thereof, e.g., in the range of 5-20.

In some embodiments, Q is in the range of 1 ms-1000 ms, or any subrange thereof, e.g., in the range of 10 ms-100 ms.

In some embodiments, the broad range of reference stimuli includes natural sound and white noise stimuli.

In some embodiments, the set of auditory neurons are light sensitized; and the step of using an output generator to activate the set of auditory neurons includes: generating a time resolved optical signal; and directing the optical signal to the second set of neurons to stimulate a response.

Some embodiments include sensitizing the second set of neurons to light

In some embodiments, the optical signal includes a spatially and temporally modulated pattern of light.

In some embodiments, the modulated pattern of light includes an array of pixels having an average pixel size of less than 0.1 mm and a pixel modulation rate of greater than 100 Hz.

In some embodiments, the step of using an output generator to activate the set of auditory neurons includes: generating a set of electrical pulses; and directing the electrical pulses to the set of auditory neurons to stimulate a response.

In another aspect, a device for improving or restoring auditory function in a mammalian subject in need thereof is disclosed, the device including: an audio receiver configured to generate an input signal in response to an audio stimulus; an encoder unit including a set of set of encoders configured to generate a set of coded outputs in response to the input signal; and an output generator configured to, in response to the set of coded outputs, activate a set of auditory neurons, where the set of auditory neurons are associated with a set of impaired signaling cells; where the second set of cells produces a response to a broad range of stimuli that is substantially the same as the response to the stimuli in an unimpaired subject.

In some embodiments, the input receiver includes an audio transducer configured to convert an audio signal to a digital signal.

In some embodiments, the encoder unit includes at least one processor.

In some embodiments, the at least one processor includes a digital signal processor.

In some embodiments, the at least one processor includes multiple processors configured to operate in parallel.

In some embodiments, the output generator includes a set of electrodes.

In some embodiments, the output generator includes an optical signal generator.

In some embodiments, the optical signal generator includes a light emitting diode array or a digital light processor.

In another aspect, a non-transitory computer readable media is disclosed having computer-executable instruction including instruction for executing steps including: generating an input signal in response to an audio stimulus; controlling an encoder unit including a set of set of encoders to generate a set of coded outputs in response to the input signal; and controlling an output generator to, in response to the set of coded outputs, activate a set of auditory neurons, where the set of auditory neurons are associated with a set of impaired signaling cells; where the set of coded outputs control the output generator to activate the set of auditory neurons to produce a response that is substantially the same as the response to the stimuli in an unimpaired subject.

Various embodiments may feature any of the elements, steps, devices, techniques, etc. described above, either alone or in any suitable combination.

The terms prosthetic, prosthesis, prosthetic device, and prosthesis device are used interchangeably herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic illustrating neural activity in a neural system, where signals from a set of cells labeled A are communicated through a set of cells labeled B to generate a response in a set of cells C.

FIG. 1B is a schematic illustrating the use of a neural prosthesis to treat impairment in the neural system of FIG. 1A.

FIG. 2 is a block diagram of a neural prosthesis.

FIG. 3 is a schematic diagram of a neural prosthesis.

FIG. 4 is a schematic diagram of a neural prosthesis featuring multiple encoders.

FIG. 5 is a functional block diagram of a processor for a neural prosthesis.

FIG. 6A is a plot of a time dependent firing rate generated by an encoder of a neural prosthesis.

FIG. 6B is a plot of a digital pulse train generated based on the time dependent firing rate shown in FIG. 6A.

FIG. 6C is a plot of the pulsed output of a neural prosthesis train generated based on the digital pulse train of FIG. 6B.

FIG. 7 is a functional block diagram of a processor featuring a parallel processing architecture.

FIG. 8A is an illustration of a neural prosthesis deployed in and on a human subject.

FIG. 8B is an x-ray snapshot of an implanted portion of the neural prosthesis of FIG. 8A.

FIG. 9 is functional block diagram of an auditory prosthesis.

FIG. 10A is a schematic of an auditory prosthesis featuring multiple output electrodes.

FIG. 10B is a schematic of an auditory prosthesis featuring multiple output light emitting diodes (LEDs).

FIG. 11A is a schematic illustration of a flexible LED array implanted in the cochlea of a subject.

FIG. 11B is a top down view of the a flexible LED array of FIG. 12A prior to implantation.

DETAILED DESCRIPTION

FIGS. 1A and 1B illustrate the operation of a neural prosthetic 100. FIG. 1A, illustrates the function of an unimpaired subject. A first set of neurons (A) sends signals to another set of neurons (B) and they, in turn, send signals to a third set (C). FIG. 1B illustrates the function in an impaired subject, e.g., where the subject has suffered a stroke that damages set B. The neural prosthetic 100, bypasses or jumps (the terms are used interchangeably herein) the impaired nerves of set B. Using experimentally derived information about the transformation implemented by the unimpaired set B in communicating signals from set A to C, one can build the device 100 such that it mimics the transformation. That is, the device 100 can produce a response in set C (e.g., a neural or nerve firing pattern) that closely mimics that which would normally occur when A sends out its signals to set C through an unimpaired set B. C can then send on normal signals to its downstream neurons, and the patient can regain normal functioning. The encoder essentially jumps over B. (If the transformation is well modeled based on experimental data, then this can be done for arbitrary signals from A). To drive the neurons in C, several techniques are possible such as driving optogenetic transducers (e.g., channelrhodopsin-2 or one of its derivatives) or electrode based stimulation, as described in greater detail below.

In some embodiments, the signal from set A may be replaced by an external stimulus. This was the case in the Retinal Application, where set B corresponded to damaged retinal cells (e.g., photoreceptors), set C corresponded to retinal ganglion cells, and the prosthesis 100 received a visual stimulus (e.g., using a camera), processed the stimulus with encoders in a way that mimicked the processing of the damaged retinal cells and circuitry (which would be analogous to B in FIG. 1B) and used a high resolution transducer to drive the retinal ganglion cells to produce a response that closely matched that produced in an unimpaired subject. A similar approach may be used for restoring or improving auditory function, as detailed below.

As noted above, in some embodiments, a data-based phenomenological approach is used in building the encoders for the prosthetic 100: In typical cases, to build the encoder, one needs to finds the transformation between the outside world (e.g., an external visual or audio stimulus) and a set of neurons or between two sets of neurons. Below are three examples.

In the case of the prosthetic device for the retina, described in the Retinal Application, the encoder mimics the transformation between visual stimuli (the outside world) and the retina's output cells—that is, it jumps over the damaged sensory cells in the retina (the photoreceptors) and interacts directly with the healthy cells (e.g., ganglion cells), the retina's output cells, so that normal signals can be sent to the brain.

In the case of an auditory prosthetic, the encoder mimics the transformation between auditory stimuli (the outside world) and the cells in the auditory nerve—that is, it bypasses the damaged sensory cells (the hair cells of the inner ear) and interacts directly with the auditory nerve cells, the spiral ganglion cells, so normal sound information can be sent to the brain.

In the case of a motor prosthetic (the specific embodiment given below), the encoder mimics the transformation between Supplementary Motor Area (SMA) and spinal motor neurons (SMN)—that is, it jumps over the damaged primary motor cortex (a area commonly damaged by strokes) and interacts directly with the healthy cells, the SMN (or the muscles they synapse on), so that normal muscle contractions/relaxations can be made.

This approach may be extended to a wide variety of other applications. A non limiting list of such applications is provided in Tables 2-6 found toward the end of this document.

To generate the data based model, the transformations performed by the encoders are worked out a priori (e.g., in an animal model or, using human patients, i.e., using electrode implants and electromyography (EMG)). It's worked out by causing a large variety of patterns of activity to occur in the system and recording from the healthy neurons.

For example, to develop the encoder for the visual prosthetic, recordings were made from retina's output neurons, the ganglion cells, while the retina was presented with a wide variety of stimuli: this allowed us to determine the transformation from visual stimuli to retinal ganglion cell firing spike patterns.

Likewise, in the case of the auditory prosthetic, recordings are made in the spiral ganglion cells in the presence of a wide variety of auditory stimuli (e.g., including white noise and natural noise), so the transformation between sound stimuli and the spiral ganglion cell spike patterns can be determined.

In the case of the motor prosthetic, one may use two sets of recordings: one from neurons in the SMA and one from the spinal motor neurons that correspond to them (e.g. to generate a set of encoders useful for arm prosthetics, one may record from SMA neurons that affect arm movements and from spinal motor neurons that control arm muscles), so one can obtain the transformation between the two sets of neurons (in this case, in FIG. 1B, set A would correspond to the SMA neurons that affect arm movements, and set C would correspond to the spinal motor neurons that control arm muscles).

In each of these example and other cases, visual, auditory, motor, or other, the approach is phenomenological: One parameterizes the relationship between the external stimuli and a set of neural signals or between two sets of neural signals, and one finds the parameter values using an optimization procedure, such as maximum likelihood.

In many applications, an advantage of this approach is that it has the capacity to generalize, that is, to mimic the processing of the impaired cells across a broad range of activity, because the approach uses a mathematical transformation to capture the relation between the outside world and a set of neurons or between two sets of neurons, rather than, for example, a look up table. As indicated schematically in FIG. 1B, the prosthesis 100 is designed to take activity patterns of arbitrary complexity in set A and produce the outputs that normally occur in set C as a result—that is, for most or all patterns that occur in A, the method will be able to make C produce its normal output (e.g., nerve firing patterns). This is advantageous because normal brain activity is complex and variable and cannot be accurately characterized into a small number of categories, as would be necessary for the more standard look up table approach.

Note, in some examples presented herein the prosthesis device is described as jumping or bypassing impaired cells. It is to be understood that in typical embodiments, the prosthetic does not simply reproduce the processing of specific impaired cells, but provides an accurate proxy of the input/output transformation that occurs in a normal subject which converts a given input stimulus or neural activity at A into an output at C. That is, the prosthetic not only mimics the behavior of a subset of impaired cells in B, but instead acts as a proxy for the entire signally chain (potentially including both healthy and/or impaired cells with various interactions) from A to C.

Motor Prosthesis

In one embodiment, the prosthesis 100 is employed to restore motor function in an impaired subject. Restoration of motor system function as is important for a number of reasons, including: a) damage to the motor system is the major source of disability in stroke and other neurological disease (e.g., MS, primary lateral sclerosis (a form of ALS), cancers of the nervous system), b) major features of the motor system's anatomy map on to the A to B to C scheme described above in reference to FIGS. 1A and 1B, and c) the motor system is readily accessible to the required studies in animals and for implants in humans. Thus, using the techniques described herein building a set of encoders for applications is straightforward, and the return on the effort is large—it can provide a remedy for a very broad range of disorders—that is, motor damage due to many different underlying causes can all be treated with the same set of encoders.

Normally, during voluntary movement, signals are transmitted from the Supplementary Motor Area (SMA) to Primary Motor Cortex (PMC) to Spinal Motor Neurons (SMN) to Muscle (M). The SMA corresponds to A in FIGS. 1A and 1B, the PMC and its descending fibers correspond to B, and the SMN (and their axons) correspond to C. In some embodiments, the SMN can be jumped also (i.e., included as part of B), and stimulation can go directly to muscle, which would then correspond to C.

In some cases, B is a particularly vulnerable part of the motor system because the pathway from PMC to the SMN is long—that is, the cell bodies of the neurons lie in the cortex, but their axons descend through the thalamus, brain stem, and spinal cord. Thus strokes or other damage to any area along the pathway will interrupt their signals and cause motor deficits or outright paralysis.

Referring to FIG. 2, in some embodiments, the prosthesis 100 is a device that carries out the transformation of signals from A to C that is normally carried out by interactions from A to B to C. The prosthesis 100 includes an input receiver 101 (e.g., one or more electrodes) which record an input signal generated by set of neurons in A (e.g., in response to a decision by the patient to make a movement, a motor command). A processor 102 (sometimes referred to herein as an encoder unit) processes the input signal using a set of encoders to generate a set of coded outputs. An output generator 103 (e.g. an electrode or optical device of the types described herein), in response to the coded outputs, activates the second set of neurons (neurons in C) to produce a response to the input signal, e.g., a response that is substantially the same as the response in an unimpaired subject.

In some embodiments, an encoder implemented by the prosthesis 100 operates using a model for the transformation, {right arrow over (c)}={right arrow over (f)}({right arrow over (a)}), where {right arrow over (a)} is the pattern of neural activity (expressed here as a n firing rate as a function of time) in region A, and {right arrow over (c)} is the pattern of neural activity in region C. Both {right arrow over (a)} and {right arrow over (c)} are multivariate (they represent the activity of a population of neurons), so we represent them here as vector-valued functions of time. (Note that it's not critical to understand B at a mechanistic level, just to capture its input/output relation, as in the retinal prosthetic approach described in the Retinal Application.)

Some embodiments employ a strategy adapted from those found to be effective in the retina—that is, we choose the following parametric form, and we determine the parameters of the form by optimizing a cost function separately for each output neuron (or small groups of output neurons, e.g., containing less than 2, less than 3, less than 5, less than 10, less than 20, less than 30, less than 50, or less than 100 neurons, e.g., in the range of 1-1000 neurons or any subrange thereof).

For example, for each output neuron, c_(i), we determine weight functions, {right arrow over (w)}_(i), and a nonlinearity, N_(i), so that the modeled transformation c _(i) ^(fit) =N _(i)({right arrow over (a)}·{right arrow over (w)})  (1) is an optimal match to the actual transformation, c_(i)=f_(i)({right arrow over (a)}), measured experimentally using the techniques described herein. N_(i) is a pointwise nonlinearity, i.e., a function y=N_(i)(x), where x and y are both real-valued quantities (in the case of the retinal encoders, N_(i) was a cubic spline with 7 knots, but any other suitable number may be used), and {right arrow over (w)} is a vector of weights, specific to the output neuron i. {right arrow over (w)}_(i) consists of an array of quantities w_(i,j)(t), where i labels a neuron in the population C, j labels a neuron in the population A, and t is time. The ith component of the dot product {right arrow over (a)} is calculated as follows: Σ_(j,t) a _(j)(t)w _(i,j)(t) As was the case for the encoders for the retina, the optimization is performed to maximize the expected log likelihood over the entire output population, namely,

$L = \left\langle {\sum\limits_{i}{{ll}\left( {c_{i}^{fit},\overset{\rightarrow}{a}} \right)}} \right\rangle$ ll(c_(i) ^(fit), {right arrow over (a)}) denotes the log likelihood that c_(i) ^(fit) accounts for the observed activity of the ith neuron in C, when {right arrow over (a)} is the pattern of neural activity in region A, and the brackets denote an average over all patterns of activity produced in A. This likelihood is calculated from Poisson statistics based on the model firing rates (i.e., c_(i) ^(fit)).

The parametric form in eq. 1 builds on what we used for the retinal transformation: the weights {right arrow over (w)}_(i), i.e., the arrays w_(i,j)(t) correspond to a set of spatiotemporal linear filters, because the subscripts i and j correspond to the positions of the neurons in C and A, respectively, and N_(i) is an adjustable nonlinearity.

This overall strategy has several advantages—the linear-nonlinear cascade (LNC) can be used as a universal building block for any transformation (Cybenko, 1989), it is a reasonable caricature of the input/output transformation carried out by single neurons (or small groups of neurons), and there are optimization techniques that work well with complex, natural inputs, such as are present in area A. In the retina, the inputs were white noise and complex natural scenes. In the motor case, the inputs are the activity patterns that occur in A under freely-moving behavior.

Constructing Encoders from Experimental Motor Activity Data

In some embodiments, the encoders implemented by the processor 102 are constructed from data collected in two locations: the SMA and the targeted muscles. Briefly, e.g., one may implant an array of extracellular electrodes in SMA (e.g., as described in Hochberg et al, 2006). This allows one to obtain firing patterns from one or more SMA neurons (e.g. in the range of 1-10,000 neurons, or any subrange thereof). At the same time, one may apply surface electrodes to the targeted muscles to obtain electromyography signals (EMGs), as mentioned above (see, e.g., Cescon et al, 2006), as this allows us to obtain the array of activity patterns, c_(i).

Note, in various embodiments, one can use the EMG from each muscle to determine the activity pattern in at least two ways: the EMG can be processed to count spikes (to obtain a total firing rate), or it can be rectified and low pass filtered. In many applications, the first approach is the simplest and corresponds directly to the population firing rate, but there are practical advantages to using the second. Specifically, the rectified, low pass filtered signal will be dominated by the larger motor units in the population, and since larger motor unit produce more force by the muscle, this low pass filtered signal correlates more closely with the force command, and, therefore, is considered the more relevant quantity when aiming to control force.

Note that for a given c_(i), some SMA neurons may not be relevant for its control, and the model described herein accounts for this (the weights of these neurons will be zero or negligible). This is analogous to the situation with ganglion cells in the retina, where some regions of an image (some pixels) are not relevant for a given ganglion cell's control, and these pixels are given negligible weights.

To generate generalizable encoders, one adapt the strategy as was used for generating the retinal encoders: one may provoke the system with a broad range of stimuli. In the case of the retinal encoders, we presented the retinas from normal subjects with two classes of stimuli-artificial (white noise) and natural scenes—and recorded ganglion cell responses. We then modeled the transformation from stimulus to response. The “training” stimuli (the white noise and natural scenes) were broad enough to produce a general model, one that was effective on any stimulus. In other words, given the training stimuli, we obtained a model that faithfully reproduced ganglion cell responses to essentially any stimuli (stimuli of arbitrary complexity).

In the case of the motor system, one may adapt the same approach. The normal subject (e.g., a human, a non-human primate, a mouse, etc.) carries out a variety of artificial and natural movements, such as walking on a wide variety of different and irregular terrains and grades, and manipulating objects of different masses, and we record responses from SMA and from the muscles (e.g., using the surface electrode EMG recordings). The irregular terrains and unpredictable loads are an example of a motor equivalent of white noise, and the movements on naturally changing terrain with predictable loads are an example of a motor equivalent of natural scenes. In typical applications, the two together are the key elements for obtaining generalizable encoders. In various embodiments, other suitable activities may be used.

Using the experimental the data sets generated in the previous step, one may model the transformation between SMA recordings and EMG recordings using eq. 1. This gives a set of encoders, e.g., one for each muscle.

An alternative strategy to the one described above is to treat c_(i) as the total firing rate of a subpopulation of neurons, rather than a single neuron. This makes sense in the case of muscle activation because each muscle is activated by a subpopulation of neurons, rather than a single neuron, and the relevant variable for the subpopulation is total firing rate (at each moment in time). This firing rate can be obtained from the branch of the peripheral nerve that innervates the muscle. Experimentally, the firing rate can be measured noninvasively using surface electrodes that are placed on the skin over the muscle; the surface electrodes record an electromyography signal (EMG), from which the firing rate of the branch can be measured. (The EMG basically a surrogate for the total firing rate in the peripheral nerve branch that innervates the muscle.)

In this alternate strategy, each subpopulation c_(i) corresponds to the population that innervates a different muscle. Thus, the transformation modeled is the transformation from the activity is SMA to {right arrow over (c)}, the pattern of activity in the array of subpopulations.

Note that despite the apparent challenges of providing a transformation that can cover the jump from supplementary motor area to spinal motor neurons (or muscle), experimental results obtained in the case of retinal prosthetic techniques indicate that these challenges can be readily overcome with the techniques described herein. In the case of the retinal prosthesis, the transformation jumped at least two synapses and captured the output almost exactly (for both mouse and primate subjects), as shown in the Retinal Application. Specifically, the transformation was from image to ganglion cell output which required jumping all the operations from photoreceptors to bipolar cells to ganglion cells, including the lateral actions of the horizontal cells and the many types of amacrine cells. The jump in the motor system would be, e.g., Supplementary Motor Layer 5→Motor Layer 4→Motor Layer 2/3→Motor Layer 5→spinal motoneuron or muscle. A second related point is that the approach might appear to be challenging due to the apparent high dimensionality in the motor context—that is, the motor cortex is signaling activity for movements related to many extremities—e.g., for the legs, it's covering the hips, knees, ankles, feet, toes, etc. But the dimensionality is not as high as it seems because we treat the cells as independent, a reasonable approximation as shown by Lee et al., 1998, Variability and Correlated Noise in the Discharge of Neurons in Motor and Parietal Areas of the Primate Cortex J. Neurosci, 18:1161; Averback and Lee (2006) Effects of Noise Correlations on Information Encoding and Decoding, J Neurophysiol 95: 3633-3644, and because many (or all) transformations are carried out locally—that is, the transformation required for knee movements are laterally displaced in the tissue from those involved in ankle movements, etc, just as they are in the retina. (In the retina, transformations for different parts of visual space are carried out locally, and the transformations can be carried out very effectively assuming conditional independence among the cells. For example, in some typical cases, each location in visual space is handled by about 10-30 cells—thus one doesn't have to perform an optimization over thousands or even hundreds of neurons to obtain a good representation. Comparison of optimizations using large populations with those using local populations pieced together as independent groups indicates that local optimization provides satisfactory results.

It should be noted that the number of degrees of freedom for a movement is far, far less than in an image, and the motor system is much more redundant. For example, in some typical situations: there are about 2×10^6 optic nerve fibers (including both eyes), but roughly about 1/10 that number of descending motor neurons (roughly 10^4 per spinal segment, 30 spinal segments.) And on a per-cell basis, in typical cases, the motor system is also more redundant: there are about 1000, at least, motor neurons per muscle, even though only one time series (the force generated by that muscle) must be specified. In some cases, for a complete comparison one needs to compare contrast sensitivity and bandwidth for vision, with motor control precision and bandwidth, but in typical case these are comparable as well (1 part in 300 for visual contrast sensitivity, motor control is, in many cases, not finer than that; e.g., corresponding to a ˜30-60 Hz bandwidth for both vision and motor.)

The greater redundancy of the motor system is also indicated by clinical and electromyographic results showing that about an 80% loss of motor neurons is typically required to have a clinical (functional) motor deficit.

Exemplary Implementation of Motor Prosthetic

Referring again to FIG. 2, the motor prosthetic 100, may incorporate encoders built using the techniques described here, e.g., implemented by the processor 102. The encoders are used in conjunction with an input receiver 101 and an output generator 103.

As described herein, in various embodiments, the strategy is to first develop encoders that capture the transformation from SMA activity to nerve branch activity (for arbitrary activity patterns), and, second, to use these encoders as an interface between SMA and the muscles (in patients in which the connections have been severed or otherwise impaired (anywhere along the pathway from SMA to muscle).

In some embodiments, what the encoders do is jump over the damaged area (bridge the gap) in real time or near real time; the muscle receives the signals or a close proxy of the signals it would normally receive—but it receives them through the device instead of the normal biological circuitry. Because the encoders mimic the normal transformations from SMA all the way down to the branches of the nerves that directly command the muscle, they can restore normal or near-normal movements.

Referring to FIG. 3, in some embodiments the input receiver 101 includes a plurality of electrodes 301 embedded in the SMA (although three electrodes are shown, any suitable number may be used).

For example, in some embodiments, electrodes may be implanted in human SMA using techniques of the type described in Hochberg et al, 2006: Action potentials (e.g., of individual neurons or small groups of neurons) may be recorded, e.g., using a 10×10 array of silicon microelectrodes (e.g., of the type known in the art as a Utah array). In one embodiment, electrodes 1 mm in length protrude from a 4 mm×4 mm platform. Signals from the electrodes then pass through a titanium percutaneous connector to reach the outside environment. The connector is then connected to a recording system, which carries out amplification and unit identification on the signals from the electrodes, e.g., using the techniques described in Chestek et al, 2009. Note that in some embodiments, one may use single unit (e.g., single cell) activity as the relevant quantity in determining SMA activity. Additional or alternatively local field potential or multi-unit activity as recorded by each electrode in the array could play this role.

The measured SMA activity signals are then fed into the processor 102 that performs the operations of the encoders. In some embodiments, the electrodes and a battery pack are positioned subcutaneously, as in deep brain stimulation (DBS) methods familiar in the art, e.g., as used for Parkinson's patients. For this, a battery pack to drive the recording system is put in subcutaneously in the anterior chest wall with leads tunneled up to a site in the scalp to supply power to the recording system. An example of such a system is described in greater detail below.

The output of the encoders is then sent to muscle via the output generator 103. As shown, the generator includes an array of output electrodes 302. Again, although three output electrodes are shown, any number may be used. In various embodiments, and suitable technique for stimulating the muscle may be used. In some embodiments, the generator may be implemented using the techniques described in Moritz et al, 2008 and/or Guiraud et al, 2006.

For example, in some embodiments, each encoder output, determines the amplitude of the current pulse during a given time bin. In some embodiments, the time bins are typically 20 ms, following standard practice for stimulating muscles at 50 Hz, however, any suitable time bin duration may be used. In some embodiments, the maximum current (peak of the current pulse) will follow standard practice (i.e., about 10 mA), however, any suitable value may be used.

In some embodiments, after device implantation, the encoder must be optimized for the specific patient. For example, in the case of encoders used in prosthetics for humans, but based on experimental data from non-human primate subjects (e.g., monkeys) the optimization makes the necessary correction, e.g., it takes into account the fact that the encoder was determined for monkey, and the SMA of a monkey and a human are not the same size. Tuning may be accomplished in software in the encoder. In some embodiments, one may add a set of additional parameters to each encoder. These parameters determine the overall location and size of the patch of input neurons corresponding to a_(j), as used in eq. 1. These parameters may be determined as follows for each target muscle: the patient is asked to attempt to execute a movement that normally results in contraction, isolated as well as possible to that muscle (note that because the patient cannot move the muscle because of neurologic damage, no movement will occur, but SMA will be activated because of the intent to move). The intent activates the neurons in the portion of the SMA that will provide the correct inputs to the encoder for that muscle. The tuning parameters are systematically varied until the muscle is in fact activated. Note that this tuning process can be expedited by functional MRI prior to implantation; this will narrow down the relevant region of SMA for each muscle.

In some embodiments, after device implantation, a gain factor that converts the encoder's output to the amplitude of the current pulse may be adjusted. This will be determined by asking the patient to make isolated movements as in the previous step, and adjusting the gain to produce the patient's desired output.

Exemplary Motor Prosthesis Device

FIG. 4 is a schematic diagram of an exemplary embodiments of the motor prosthesis 100. As shown the input receiver includes nine input devices 401 (e.g., electrodes) for measuring the activity of single neurons or groups of neurons in the SMA. The input signal measured by each input device 401 is sent to a corresponding encoder in the processor 102 (each encoder is represented as a vertical column).

The output of each processor is used to control a corresponding output generator element (e.g., an electrode, digital micromirror device element, or LED, as detailed below) 403 of the output generator 103. The output of the output generator elements drive a response in corresponding muscle or SMN cells.

Execution of the encoders proceeds in a series of steps, indicated in the figure as modules 402 a-c: preprocessing 402 a, spatiotemporal transformation 402 b, and spike generation 402 c. The output of the spike generation step may be nontransiently stored in a storage module 402 d in preparation for conversion to a format suitable output, which may include a burst elimination step (not shown). The output is generated by the output generator 103. Note that output may be in the form of current pulses delivered as in as in either Moritz et al, 2008 or Guiraud et al, 2006 as is standard practice for stimulating muscles. Arrows show the flow of signals from specific regions of the SMA through the modules of the encoders, through output generator 103, which drives muscles or SMN.

Input Receiver

As noted above, in some embodiments, electrodes may be implanted in human SMA using techniques of the type described in Hochberg et al, 2006: Action potentials (e.g., of individual neurons or small groups of neurons) may be recorded, e.g., using a 10×10 array of silicon microelectrodes (e.g., of the type know in the art as a Utah array). In one embodiment, electrodes 1 mm in length protrude from a 4 mm×4 mm platform. Signals from the electrodes then pass through a titanium percutaneous connector to reach the outside environment. The connector is then connected to a recording system, which carries out amplification and unit identification on the signals from the electrodes, e.g., using the techniques described in Chestek et al, 2009. Note that in some embodiments, one may use single unit (e.g., single cell) activity as the relevant quantity in determining SMA activity. Additional or alternatively local field potential or multi-unit activity as recorded by each electrode in the array could play this role.

In other embodiments, any other suitable technique for measuring SMA activity may be used.

Processor/Encoder

As noted above, in the case of a motor prosthetic (the specific embodiment given below), the encoder mimics the transformation between Supplementary Motor Area (SMA) and spinal motor neurons (SMN)—that is, it jumps over the damaged primary motor cortex (a area commonly damaged by strokes) and interacts directly with the healthy cells, the SMN (or the muscles they synapse on), so that normal muscle contractions/relaxations can be made. These encoders use an algorithm that converts input signal from the SMA into patterns of electrical signals that are the same, or substantially similar, to that would be output in a normal subject. That is, the encoders jump all cells and circuitry between the input cells (corresponding to A in FIG. 1B) and the output cells (corresponding to C in FIG. 1B).

The prosthetic can use multiple encoders which can be assembled in a parallel manner as shown, for example, in FIG. 4, where different segments of the SMA activity are run through separate encoders, which, in turn, control different, specified output generator elements 403. In this embodiment, each encoder may have parameters suited for its operation, which may, for example, take into account the location and/or type of signaling cells being emulated by the encoder or being driven by the encoder's output. The term “code” can refer to a pattern of electrical pulses that corresponds to a pattern of action potentials (also referred to as spike trains) that the output cells produces in response to a stimulus or signals from upstream neurons. The term “code” may refer to bit streams corresponding to a pattern of spike trains. Each bit may correspond to the activity of one neuron (e.g., 1 means the neuron fires; 0 means the neuron does not fire). In other embodiments the bits correspond to other information (e.g., the firing rate of a population of neurons). The code may also be a continuous wave. Any type of waveform may be encompassed by the present invention, including nonperiodic waveforms and periodic waveforms, including but not limited to, sinusoidal waveforms, square waveforms, triangle waveforms, or sawtooth waveforms.

FIG. 5 shows a functional block diagram illustrating an exemplary embodiment of an encoder in the processor 102. As shown, the processor 102 includes a number of processing modules corresponding to the encoder, each operatively connected with one, several, or all other modules. The modules may be implemented on one or more processing devices (e.g., as described in detail below). As used herein, a module is considered to be substantially implemented on a given processor if substantially all essential computations associated with the function of the module are carried out on the processor.

The processor 102 includes a preprocessing module 501 which receives an input signal from the input receiver 101 and, e.g., rescales the signal for processing. In some embodiments, the preprocessing module implements processing analogous to that described in the Retinal Application subsection entitled “Preprocessing Step.”

A spatiotemporal transformation module 502 receives the output of the preprocessing module and applies a spatiotemporal transformation (e.g., analogous to that described in the subsection of the Retinal Application entitled “Spatiotemporal Transformation Step”) to generate, e.g., a set of firing rates corresponding to those that would have been generated by the output cells, e.g., to a digital pulse generator. In some embodiments, the spatiotemporal transformation module 502 includes a spatial transformation module 502 a that convolves the input signal with a spatial kernel and a temporal transformation module 502 b that convolves the output of the spatial transformation module 502 b with a temporal kernel to generate a temporal transformation output. In other embodiments, e.g., where the processing involves an encoder with a non-separable spatiotemporal transformation, separate spatial and temporal transformation modules are not used.

In some embodiments, the processor 102 includes a nonlinear transformation module which 503 applies a nonlinear function to the spatiotemporal transformation output to generate the set of firing rates (e.g., as described in reference to Eq. 1 above). In some embodiments the nonlinear function is implemented using a look-up table.

A digital pulse generator module 505 generates digital pulse trains corresponding to the firing rates output from one or more of the other modules and generates a digital pulse train (i.e., a series of digital pulses) corresponding to each firing rate. These pulse trains are then output to the output generator 103. In some embodiments, the digital pulse generator module 505 implements processing of the type described in the subsection of the Retinal Application entitled “Spike Generation Step.”

FIGS. 6A-6C show an example of the generation of a spike train output based on a calculated firing rate. FIG. 6A shows the time dependent firing rate calculated by the encoder. FIG. 6B shows the corresponding spike train generated by the pulse generator module 505. FIG. 6C shows the corresponding output of the output generator 103.

Referring back to FIG. 5, in some embodiments, an interpolation module 506 is used to generate data having temporal resolution higher than the measurement rate of the input receiver 101. In one embodiment, the interpolation module 506 receives output from the spatiotemporal transformation module 502, applies interpolation, and passes the results on to the nonlinear transformation module 503. In other embodiments, the interpolation may be applied after the nonlinear transformation, e.g., to directly interpolate firing rates prior to input into the digital pulse generator 506. In some embodiments, the interpolated information has a temporal resolution corresponding to at least 2, at least 5, at least 10, at least 20, or at least 50 times or more the measurement rate of input receiver 101.

In some embodiments, a burst elimination module 507 is provided which operates on the output of the digital pulse generator module 505 to reduce or eliminate the presence of bursts. In some embodiments, the burst elimination module 507 implements burst elimination processing analogous to the type described in the subsection of the Retinal Application entitled “Spike Generation Step.”

FIG. 7 shows an exemplary embodiment of the processor 102 featuring a dual processor architecture. As shown, the processor 102 includes a general purpose processor (GPP) and a digital signal processor (DSP), e.g., integrated onto a single chip. The GPP and DSP are connected to a shared memory (MEM). The processor 102 receives data from input receiver 101, e.g., via the shared memory. The processor 102 outputs data, e.g., to the output generator 103.

In one embodiment, the DSP is a Texas Instrument TMS320C64 series processor. The GPP is an ARM Cortex A8 processor, and the shared memory is an SDRAM (e.g., with 512 MB of memory). In various embodiments, other suitable processors known in the art may be used. Some embodiments may feature more than two parallel processors and more than one shared memory.

The platform shown in FIG. 7 is capable of highly-parallel computation. The processing flow may be pipelined, as described above, with the implementation of various processing steps or modules divided between the processors. In general, the more computationally expensive processing tasks (e.g., tasks involving complicated matrix operations, convolutions, interpolation etc.) may be assigned to the DSP, with less expensive tasks (e.g., scaling operations, pulse generation, process synchronization and other “housekeeping” tasks, etc.) may be assigned to the GPP.

The table below shows an exemplary assignment of the processing steps. However, in other embodiments, different assignments may be made.

TABLE 1 Dual Processor Assignments Processing Step Processor Assigned Preprocessing GPP or DSP Spatial Transformation DSP Temporal Transformation DSP Interpolation DSP Nonlinearity GPP Digital Pulse Generation GPP Burst Elimination GPP Output GPP

In some embodiments, one, several, or all of the preprocessing module, the spatiotemporal transformation module, and the interpolation module are all substantially or entirely implemented of the DSP. In some embodiments, one, several, or all of the scaling module, nonlinear transformation module, the digital pulse generation module, and the burst elimination module may be substantially or entirely implemented of the GPP. This implementation of the modules may lead to a particularly advantageous processing throughput and reduced processing time. However, in various embodiments, other suitable implementations may be used.

Although some exemplary embodiments of a processor for the prosthetic device 100 are set out above, it is to be understood that in various embodiments, other processing devices may be used. The processing device, e.g., hand-held computer, can be implemented using any device capable of receiving a data and transforming them into output with acceptable speed and accuracy for the application at hand. This includes, but is not limited to, a combination general purpose processor (GPP)/digital signal processor (DSP); a standard personal computer, or a portable computer such as a laptop; a graphical processing unit (GPU); a field-programmable gate array (FPGA) (or a field-programmable analog array (FPAA), if the input signals are analog); an application-specific integrated circuit (ASIC) (if an update is needed, the ASIC chip would need to be replaced); an application-specific standard product (ASSP); a stand-alone DSP; a stand-alone GPP; and the combinations thereof.

In one embodiment, the processing device is a hand-held computer (Gumstix Overo, Gumstix, San Jose, Calif.), based around a dual-core processor (OMAP 3530, Texas Instruments, Dallas, Tex.) that integrates a general purpose processor (GPP) and a digital signal processor (DSP) onto a single chip. This platform is capable of highly-parallel computation and requires much less power than a typical portable computer (˜2 Watts or less, compared to 26 Watts for a standard laptop computer). This allows the transformation to be computed in real-time, on a device that is portable and can be powered on a single battery for long periods of time. For example, typical laptop batteries, with charge capacities in the range of 40-60 Watt-hours, could run the processor continuously for about 20-30 hours. In another embodiment, all or a portion the processing device is small in size so that it can be worn by a patient (as detailed below). In other embodiments, other suitable computing devices may be used, e.g., a Beagleboard device available from Texas Instruments of Dallas, Tex.

Output Generator

As described in the device component of the Retinal Application, the encoder or encoders could drive many output elements. Several output generator interfaces for driving target cells are possible.

For example, in some embodiments, the cells to be driven by the output of the prosthetic 100 (i.e., the set C shown in FIG. 1A) may be sensitized to light, e.g., using a light-activated transducer (such as Channelrhodopsin-2). The output generator 103 could be an LED array, a set of fiber optics driven by an LED, a digital light processing (DLP) device, among others.

These optical devices would output pulses of light that correspond to the activity patterns of the cells in C. The pulses of light would drive the light-activated transducer, causing the cells in C to fire as the encoder specifies. For example, the encoder would send signals to a general purpose input/output (GPIO), which would signal the LEDs.

For example, in some embodiments, an encoder's output is a set of spike times (times at which an action potential should be produced in the downstream neuron). Because the output is in a sense binary (at each moment in time, a spike does or does not occur), this can be naturally converted into a program that sends high/low information to the GPIO. The GPIO then outputs voltage that is “high” and turns the LED on, or “low” and does not turn it on. In other words, the encoder produces a set of spike times, which get converted into TTL pulses through the software and the GPIO, and pulses current then goes down a wire from the GPIO to the LED. The temporal resolution of the spike times produced by the encoder may be sub-millisecond or any other suitable value.

The TTL pulses are the length of the neural signal (e.g., about 1 ms for an action potential.) In this example, the LEDs are separately addressable (one for each encoder); however, other methods that allow better use of interface materials (data compression), such as multiplexing or making use of correlations in the pulse patterns of the encoders to get many signals through to many LEDs rapidly, may be used. Finally, the addition of an amplifier to drive up signals to the LEDs may be built in as well (to allow the neurons receiving the light pulses to fire in a one-to-one manner or a near one-to-one manner with the pulses they receive).

For output generators based on electrodes, the output generator could consist of any device capable of driving current into the electrodes.

In general, as will be apparent to one skilled in the art, for various applications, any of the output generation techniques described in the Retinal Application may be adapted for use in the devices described herein.

Exemplary Deployment of the Motor Prosthesis on a Human Subject

Referring to FIG. 8A, in one embodiment of the motor prosthetic 100, electrodes of the input receiver 101 are implanted in SMA. The electrodes and a battery pack are all subcutaneous, as in deep brain stimulation (DBS) methods used for Parkinson's patients. The battery pack to drive input receiver 101 is put in subcutaneously in the anterior chest wall; it has leads that are tunneled up to a site in the scalp so it can supply the needed power to the recording system.

The signals from the input receiver are sent wirelessly to the processor 102, implemented in a unit worn on a belt with its battery pack.

The processor then drives output generator 103, as shown implemented as an implanted muscle stimulator system which is also completely internal to the human subjected including a power source. In some embodiments, the stimulator system may be of the type described in Guiraud et al, 2006. In some embodiments, the dimensions of the stimulator are comparable to the battery pack used for pacemakers (e.g., about 6 cm×6 cm). In one embodiment, including the connectors to muscle, the width of the stimulator is about 10 cm. FIG. 8B shows an X-ray snapshot from Guiraud et al, 2006, showing actual size of an exemplary stimulating device inside a human.

Procedures for Measuring Motor Prosthetic Performance

The following describes exemplary procedures for measuring the performance of the prosthetic 100 and its encoders. Performance of the encoders can be measured on a forced choice activity discrimination task or performance on an error pattern test. The term “test stimulus” that will be used herein, refers to pattern of muscle activity, measured using EMG.

To evaluate performance on a forced choice discrimination task, a known test in the art, a confusion matrix is used (Hand D J. 1981). A confusion matrix shows the probability that a pattern of nerve branch activity ({right arrow over (c)}, the population comprising the individual activities of each subpopulation c_(i)) corresponds to its appropriate pattern of SMA activity, {right arrow over (c)}_([k]). To generate different patterns of SMA activity, the animal (or human) is required to carry out an array of stereotyped movements (e.g., moving a cursor to one of several locations on a computer monitor). Each kind of movement (for example, the movement to each location) is repeated for many trials, thus giving a set of SMA activities. For each movement type k, the set of SMA activities is denoted {right arrow over (a)}_([m]), and the set of resulting nerve branch activities is denoted {right arrow over (c)}_([k]).

With respect to the matrix, the vertical axis gives the movement type k. The horizontal axis gives the movement type predicted by decoding the pattern of nerve branch activity {right arrow over (c)}_([k]); the decoded movement type is denoted m. The matrix element at position (k,m) thus gives the probability that nerve branch activity {right arrow over (c)}_([k]) is decoded as movement type m. If m=k, the nerve branch activity pattern is decoded correctly, otherwise, it is decoded incorrectly. Put simply, elements on the diagonal indicate correct decoding; elements off the diagonal indicate confusion.

To generate the confusion matrices, we divide the data into two sets: a training and a testing set. The training set is obtained in order to build response distributions, and the testing set is obtained for decoding.

To decode each pattern in the test set, {right arrow over (c)}_([k]), we determine the pattern of SMA activity that was the most likely to have produced it. That is, we determine the pattern {right arrow over (a)}_([m]) for which

$p\left( {\overset{\rightarrow}{a}}_{\lbrack m\rbrack} \middle| {\overset{\rightarrow}{c}}_{\lbrack k\rbrack} \right)$ was maximal. Bayes' theorem is used, which states that

${{p\left( {\overset{\rightarrow}{a}}_{\lbrack m\rbrack} \middle| {\overset{\rightarrow}{c}}_{\lbrack k\rbrack} \right)} = {{p\left( {\overset{\rightarrow}{c}}_{\lbrack k\rbrack} \middle| {\overset{\rightarrow}{a}}_{\lbrack m\rbrack} \right)}{{p\left( {\overset{\rightarrow}{a}}_{\lbrack m\rbrack} \right)}/{p\left( {\overset{\rightarrow}{c}}_{\lbrack k\rbrack} \right)}}}},$ where p ({right arrow over (a)}_([m])|{right arrow over (c)}_([k])) is the probability that the pattern {right arrow over (a)}_([m]) in the SMA was present, given that the particular {right arrow over (c)}_([k]) was present in the nerve branches. p({right arrow over (c)}_([k])|{right arrow over (a)}_([m])) is the probability that a particular {right arrow over (c)}_([k]) occurred given a particular {right arrow over (a)}_([m]), and p({right arrow over (a)}_([m])) is the prior probability of {right arrow over (a)}_([m]). p({right arrow over (a)}_([m])) is set uniform in this experiment and so, by Bayes Theorem, p({right arrow over (a)}_([m])|{right arrow over (c)}_([k])) is maximized when p({right arrow over (c)}_([k])|{right arrow over (a)}_([m])) is maximized. When p({right arrow over (a)}_([m])) is uniform, as it is here, this method of finding the most likely pattern {right arrow over (a)}_([m]) given a pattern {right arrow over (c)}_([k]) is referred to as maximum likelihood decoding (Kass et al. 2005; Pandarinath et al. 2010; Jacobs et al. 2009). For each occurrence of a movement type k that that was decoded as m, the entry at position (m,k) in the confusion matrix is incremented.

To build the distributions needed for the decoding calculations used to make the confusion matrices (i.e., to specify p({right arrow over (c)}_([k])|{right arrow over (a)}_([k]))), the procedure is as follows. As mentioned above, the subject makes N types of movements (where N is typically 8), and each is repeated many times (e.g., >20 times). For each movement, we obtain a pattern of SMA activity {right arrow over (a)}_([k]), which we record via the implanted electrodes, and we obtain a pattern {right arrow over (c)}. Each pattern {right arrow over (a)}_([k]) is taken as the spike train spanning from ˜1 sec prior to movement onset to ˜200 ms following movement onset, and binned with 10-100 ms bins. Each pattern {right arrow over (c)}_([k]) is taken as the nerve branch activity over the same period and binned in the same way. In both cases, the spike generation process is assumed to be an inhomogeneous Poisson process, and the probability of any given pattern of activity for the entire period is calculated as the product of the probabilities for each bin. The probability assigned to each bin is determined by Poisson statistics, based on the training set response in this bin. Note that this can be done by averaging over all trials for a given type of movement pattern, or by considering each trial individually.

Once the confusion matrices are calculated, overall performance in the forced choice activity discrimination task is quantified by “fraction correct”, which is the fraction of times over the whole task that the decoded movement type m was correctly matched to the movement type k.

Given this procedure, at least 3 sets of analyses may be performed. For each one, the activity patterns from the normal subject are used for the training set and a different set of activity patterns is used for the test set, as outlined below:

(1) The first set should consist of the test sets described above, i.e., out-of-sample activity patterns from the normal subject. (These are recordings of activity patterns in SMA and the nerve branches that were not used to make the training set.) We use the fraction correct produced by the activity patterns from normal subjects as the baseline correct performance.

(2) The second set should consist of the responses from the encoders. These are the responses {right arrow over (a)}_([k]) calculated from eq. 1, from the recorded SMA activity patterns {right arrow over (a)}_([k]). Responses from this test set yield a measure of how well the encoders perform, given the training set response distributions used for analysis (1). The reason for performing the analysis this way is that we want to compare the encoder's performance against the normal baseline condition.

When responses from the encoder are used as a test set, one obtains a measure of how well the motor system would do with our proxy of the transformation from SMA to the peripheral nerve branch activity (our proxy of the motor system code).

(3) The third set, which is carried out only in subjects in which the normal pathway from SMA to muscle has been damaged, is to determine the confusion matrix that relates the movement actually made, to the movement that was intended. Since the normal pathways are damaged, the movement results from applying the prosthetic's encoder signals, determined as in analysis (2), to the muscles via the output generated (in this example, output uses electrodes, see above). The movement intended is determined from the subject's verbal responses, and can be verified by decoding the patterns of activity in SMA that are produced at the time of intention. This analysis provides a measure of how well the prosthetic performs after its output has been passed through the to real tissue. This is a bottom-line measure of the prosthetic's performance in patients.

The encoder's performance and prosthetics performance in the forced choice discrimination task, as measured by “fraction correct”, will be at least about 35%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, or 100% of the performance of the normal system, or better than the normal system, measured as described above. Moreover, these performance levels may be obtained with response time for the prosthetic 100 which is substantially the same as those found in an unimpaired subject. That is, in some embodiments, the prosthetic 100 in jumping impaired signaling cells introduces a lag time of which is of suitably short duration. For example, in some embodiments the lag time is less than a factor of 5, 4, 3, 2, 1, 0.5, 0.1, (e.g., a factor in the range of 0.1-5 or any subrange thereof) or less times the signaling time exhibited by a normal subject.

Although a number of examples of neural prosthetic devices and techniques have been presented above, it is to be understood that numerous modifications are possible.

For example, the above description of the strategy to find the transformation to be implemented by the encoders; there are a number of variations that may be used.

In various embodiments, there are options for how spike trains (either input from A or output to C) are represented. For example, they can be represented as a point process in continuous time; they can be smoothed into continuous rate functions, and they can be binned. In typical embodiments, the data collected at both A and C is in the form of a point process, but under some conditions it is easier to perform optimizations with smooth representations.

The smooth representations, then, can be reconverted to spike trains by assuming, for example, Poisson spike generation. Related to this, note that some embodiments can also use a non-spiking measure to capture the neural activity in area A, such as a local field potential or optically recorded signal. These represent a local average of neural activity, and (e.g., in circumstances that do not require resolution at the single neuron level), may provide a more stable measurement. In these cases, the smooth representation of activity in A is used directly for determining the transformation.)

In various embodiments, there are options for the cost function to be used in optimization of the encoder transformation model. The examples presented above use the likelihood, because it is well principled, but under many circumstances, a mean-squared-error is an excellent approximation and the optimization is faster to perform. Note, in regard to the previous paragraph, that in order to use mean-squared-error, some form of binning or smoothing is required.

In various embodiments, while the choice of a linear-nonlinear cascade is a natural and principled one, other functional forms may also be applicable, such as models with dynamic gain controls or neural network models, or any transformation that can be expressed, explicitly or implicitly, as a solution of a system of integral, differential, or ordinary algebraic equations, whose form and coefficients are determined by experimental data. This also includes models in which activity among the neurons in region C is correlated, e.g., via recurrent feedback.

Further, while in may cases it is most straightforward to fit the model parameters for each neuron in A independently, in some applications these parameters may have a systematic dependence on the neuron's location. Identification of this dependence will reduce the number of independent parameters that must be fit, and, potentially, allow for generalization of the model to neurons not actually recorded.

Also, it is notable that in embodiments where the prosthesis device performs a jump to muscle, an involved extra transformation (e.g., from SMA output to muscle response) is likely linear, so the cascade becomes linear nonlinear linear (LNL) transformation to go from SMA to muscle.

Auditory Prosthesis

In several of the examples above, a prosthesis 100 is described which is used to restore or improve communication from one set of healthy cells A to another set of health cells C by jumping a set of impaired signally cells B that separate the sets of healthy cells.

As noted above, in some embodiments, the input signal to the prosthesis is an external stimulus instead of the activity of the set of healthy cells A. For example, FIG. 9 shows an auditory prosthetic 200. The prosthetic 200 is a device to bypass damaged hair cells in the inner ear, that is, to jump from sound stimuli directly to the output of the cochlea (the output of the spiral ganglion cells).

The prosthesis 200 includes an input receiver 201 for detecting an audio signal (e.g., a microphone or other sound transducer) an converting the audio signal to, e.g., a digital format. A processor 202 (sometimes referred to herein as an encoder unit) processes the input signal from the input receiver 201 using a set of encoders to generate a set of coded outputs. An output generator 203 (e.g. an electrode or optical device of the types described herein), in response to the coded outputs, activates auditory nerve cells (e.g., spiral ganglion cells) to produce a response to the audio stimulus, e.g., a response that is substantially the same as the response in an unimpaired subject.

A well functioning auditory prosthesis, e.g., one that can provide near normal or normal function in an impaired subject is advantageous because the incidence of hearing loss as the population ages is very high.

As in the examples presented above, an important aspect of the prosthetic 200 is the functioning of the encoders implemented by the processor 202. These are the components that carry out the transformation from sound to cochlear output. As discussed above for the motor prosthetic and for the visual prosthetic described in the Retinal Application, an advantage of this approach is that it has the capacity to generalize. it does this by using a mathematical transformation that captures the relation between the outside world (in this case, audio stimuli) and the activity of a set of neurons. The techniques used here are directly analogous to those used to generate the motor encoders described above and visual encoders described in the Retinal Application. The following discussion uses the notation developed herein for the motor encoders for convenience. However, one skilled in the art will recognize that the formalism and techniques used in the Retinal Application may be readily adapted to the present case of an auditory prosthesis.

Again the approach for building the encoders is phenomenological: One may parameterize the relationship between the external stimuli and a set of neural signals or between two sets of neural signals, and find the parameter values using an optimization procedure, such as maximum likelihood.

Note that in various embodiments, the prosthetic 200 uses signaling that is not limited to frequency coding or intensity coding, but uses natural coding derived directly from data. That is, each encoder is essentially a complete model for the input/output relations for a class of SG cell, where the input is the sound stimului (such that the hair cells are being jumped). This means it has the capability to transform any sound stimulus into the normal auditory output for that class of cell. The encoders of the prosthesis 200 thus carry much more information that simple frequency detectors and transmitters (e.g., of the type described in Boyden, 2010, U.S. Pat. Pub. No. 2010 0234273).

Constructing Auditory Encoders Using Experimental Data:

In some embodiments, encoders are constructed from data collected from spiral ganglion (SG) neurons in an unimpaired subject while sound stimuli are presented. In some embodiments, one may implant an array of extracellular electrodes e.g., using the techniques described in Sellick 1982. Accordingly one obtain firing patterns from an array of SG neurons. At the same time, one may present sound stimuli. One may formalize the relation between the sound stimuli and the SG responses as {right arrow over (c)}={right arrow over (f)}({right arrow over (a)}), where {right arrow over (a)} is vector representing the sound (as a time series), and {right arrow over (c)} is the pattern of neural activity in the SG neurons. Note that {right arrow over (a)} is univariate (it's a vector where the components are sound (pressure) as a function of time) and {right arrow over (c)} is multivariate (as above, it represents the activity of a population of neurons, the SG neurons). In some embodiments, the transformation may instead operate on the frequency spectrum of the sound. As will be readily understood by one skilled in the art, such a frequency spectrum may easily be obtained from {right arrow over (a)} via a Fourier transform (e.g., performed by processor 202 implementing an algorithm such as the well known Fast Fourier Transform).

To generate generalizable encoders, one may use the same strategy discussed herein for generating the retinal encoders and the motor encoders. One may provoke the system with a broad range of stimuli. In the case of the retinal encoders, we presented the retinas from normal subjects with two classes of stimuli—artificial (white noise) and natural scenes—and recorded ganglion cell responses. We then modeled the transformation from stimulus to response. The “training” stimuli (the white noise and natural scenes) were broad enough to produce a general model, one that was effective on any stimulus. In other words, given the training stimuli, we obtained a model that faithfully reproduced ganglion cell responses to essentially any stimuli (stimuli of arbitrary complexity).

Here, with the auditory system, one may take the same approach. On may present white noise (WN) and natural sound (NS) stimuli, where the latter falls into two categories, environmental sound and sound relevant to language (both described in, for example, Lewicki, 2002.

Given the data sets generated in the previous step, one may model the transformation between the sound stimuli and the SG responses. This provides a set of encoders, e.g., one for each SG cell or corresponding to a small group of SG cells (e.g., containing less than 2, less than 3, less than 5, less than 10, less than 20, less than 30, less than 50, or less than 100 cells, e.g., in the range of 1-1000 cells or any subrange thereof).

As for the motor prosthetic, one may use the following parametric form, and determine the parameters of the form by optimizing a cost function separately for each SG neuron: for each SG neuron, c_(i) determine weight functions, {right arrow over (w)}_(i), and a nonlinearity, N_(i), so that the modeled transformation c_(i) ^(fit)=N_(i)({right arrow over (a)}·{right arrow over (w)}) is an optimal match to the actual transformation, c_(i)=f_(i)({right arrow over (a)}). N_(i) is a pointwise nonlinearity, i.e., a function y=N_(i)(x), where x and y are both real-valued quantities (in the case of the retinal encoders, N_(i) was a cubic spline with 7 knots, but any suitable choice may be used), and {right arrow over (w)} is a vector of weights, specific to the output each SG neuron i. {right arrow over (w)}_(i) consists of an array of quantities w_(j)(t), where i labels a neuron in the SG population, and t is time. The ith component of the dot product

is calculated as follows: Σa(t)w _(i)(t) Note that this differs slightly from the parallel equation for the motor prosthetic in that it has no subscript j; this is because the quantity a here is a one-dimensional function of time (or frequency in the case where an Fourier transform has been applied). As was the case for the encoders for the retina and motor systems, the optimization is performed to maximize the expected log likelihood over the entire output population, namely,

$L = \left\langle {\sum\limits_{i}{{ll}\left( {c_{i}^{fit},\overset{\rightarrow}{a}} \right)}} \right\rangle$ ll(c_(i) ^(fit), {right arrow over (a)}) denotes the log likelihood that c_(i) ^(fit) accounts for the observed activity of the ith neuron in SG, when {right arrow over (a)} is the sound input, and the brackets denote an average over all inputs. This likelihood is calculated from Poisson statistics based on the model firing rates (i.e., c_(i) ^(fit)).

The weights {right arrow over (w)}_(i), i.e., the arrays w_(i,j)(t) correspond to a set of linear filters, one for each neuron i in the SG, and N_(i) is an adjustable nonlinearity for neuron i.

Exemplary Implementation of Auditory Prosthesis

Referring again to FIG. 9, the auditory prosthesis 200 may incorporate encoders built using the techniques described here, e.g., implemented by the processor 202. The encoders are used in conjunction with an input receiver 201 (e.g., a microphone) and an output generator 203 which stimulates a response in the SG cells.

As described herein, in various embodiments, the strategy is to first develop encoders that capture the transformation from audio stimulus to SG activity (for arbitrary activity patterns), and, second, to use the coded output these encoders to jump impaired cochlear hair cells and directly stimulate the SG cells to restore normal or near normal function.

In various embodiments, the output generator 203 may include any suitable technology for stimulating SG neurons, such as that of they type describe in Zeirhofer et al., 1995 or Zeng et al, 2009. For example, in the embodiment shown in FIG. 10A the prosthesis 200 works as follows: 1) a microphone included in the input receiver 201 sends signals to a processor 202, 2) the signal processor 202 converts the signals from the microphone to signals to drive an array of electrodes in output generator 203, and 3) the signals from processor 202 control the electrodes that stimulate the SG neurons.

As shown, the microphone and a signal processing portion 202 a of the processor 202 are located outside of the subject. The signal processing portion 202 a generated coded outputs, and transfers them, e.g., via a radio frequency (RF) or other wireless link to a subcutaneously implanted portion 202 a of the processor 202. The implanted portion receiver the signal and controls the electrodes to stimulate the SG cells.

In other embodiments, all of the processing may occur externally, with an RF signal being used to directly drive implanted electrodes. In various embodiments other implementation schemed may be used. In some embodiments, an external power supply provides power to the subcutaneous elements, e.g., via an RF or inductive power coupling, or any other power transmission technique known in the art.

FIG. 10B shows a variant of the device of FIG. 10A, where the output of the encoders is sent not to electrodes, but to light emitting diodes (LEDs) included in the output generator 203 (or another light sources) to drive alternate transducers, e.g., channelrhodopsin-2 (ChR2) used to sensitive the SG cells. Pulses from the LEDs are used to drive a response in the sensitized SG cells.

In various embodiments, expression of ChR2 or other transducer genes in SG neurons can be achieved using the gene promoters described in Table 1 of Liu et al, 2007. Examples include EF-1\alpha, NSE, CMV, CAG; these all express in SG neurons. In various embodiments, any other suitable promoters may be used.

With respect to delivery of the gene, any of the same gene therapy approaches described in the Retinal Application can be used for delivery to SG cells. Lentivirus (LV), adenovirus-5 (Ad-5) and adeno-associated virus-2 (AAV-2) have been shown to penetrate, although (Ad-5) was found to be the most effective (under conditions where the round window of the cochlea, one of the openings to the inner ear, was left intact (Lei et al, 2010). If the round window is partially digested, then AAV-2 becomes effective (Wang et al, 2011); this is valuable in some applications, as AAV-2 is one of the more promising gene therapy vectors in terms of safety (Simonelli et al, 2010). In various embodiments, any other suitable delivery technique may be used.

Referring to FIGS. 11A and 11B, in some embodiments, the output generator 203 includes a thin flexible LED array 1201 implanted in the cochlea 1202 of a subject. The flexible array is able to conform to the spiral shape of the cochlea, such that the LEDs may be positioned to stimulate SG cells. Note that although one possible configuration of the array 1201 is shown, and other suitable positioning, array size, etc.

In some embodiments, the array 1201 includes an arrays of interconnected, ultrathin LEDs 1203 that are built into a flexible waterproof material. In various embodiments, the device can be placed into the inner ear to stimulate the ChR2-expressing SG cells. In one embodiment, each LED is 100 by 100 microns, which would stimulate multiple cells; however, one can narrow its light path to target fewer cells by masking a portion of the LED. In some embodiments, the size of the masking may be optimized to allow sufficient intensity to reach the ChR2; “sufficient intensity” is defined as that which produces action potentials that follow the output of the encoder in a one-to-one or near one-to one manner.

In some embodiments, the flexibility of the array 1201 matches well with the curvature of the cochlea 1201: For example, in humans the radius of curvature of the cochlea ranges from 4 mm at the high frequency end to 0.7 mm at the low frequency end, while in some embodiments, the radius of curvature of the flexible device is e.g., 0.4 mm or less.

In some embodiments, the array 1201 may be of the type described in Kim et al, 2010. In various embodiments, the array 1201 may be operatively connected to the processor 202 using any suitable technique including, e.g., a wired or wireless connection.

In some embodiments, after device implantation, the encoder may be optimized for the specific patient. Two examples of optimization are the following. First, in some cases, different encoders capture different information (e.g., frequencies, intensity), so they need to be positioned on the SG neuron array to stimulate the appropriate SG cells (the SG cells that carry the same information). Second, in some embodiments, threshold levels and maximum levels have to be determined. This can be achieved using extracellular electrodes (e.g., using pure tones to drive a small number of cells at a time).

Methods for Measuring Auditory Prosthesis Performance

The following describes exemplary procedures for measuring the performance of the prosthetic 200 and its encoders. In some embodiments, the procedure for measuring the performance of the encoders and the prosthetic will follow directly from that used to test the retinal prosthetic or motor prosthetic, focusing specifically on performance on a forced choice discrimination task. The term “test stimulus” that will be used herein, refers to a stimulus or a stimuli, which is presented to an animal for evaluation of performance of the encoders or encoders and output generator (e.g., the auditory prosthesis 200).

In various embodiments, it is important that the task used to measure prosthetic performance falls into a range of difficulty that allows meaningful information to be obtained. Briefly, the task must be difficult enough (i.e. must use a stimulus set rich enough) that the normal retinal responses provide information about the stimuli, but do not perform perfectly on the task. For example, in the task shown in Example 8 in the Retinal Application, the fraction correct using the responses from the normal retina, was 80%, satisfying this criterion. If the task used had been too hard, such that the normal retina's performance were near chance, then matching would have been of limited use to a performance analysis. Conversely, if the task chosen had been too easy (e.g., requiring just gross discriminations, such as black versus white, and where the fraction correct for the responses from the normal is near 100%), then prosthetic methods that are far from approximating the natural code and provide nothing close to normal vision would appear to do well. The same applies to the auditory tests: it is critical to use an appropriately challenging test, as was used in the examples in the Retinal Application. The use of a challenging test also allows one to determine if the prosthesis is performing better than the auditory system (i.e., entering into the domain of “bionic hearing”).

Various methods for the forced choice task follow directly from those analogous used in the Retinal Application, converting to auditory stimuli. Two types of natural stimuli may be used—natural environment sound stimuli and speech-sound stimuli, as described in, for example, Lewicki, 2002. To evaluate performance on a forced choice discrimination task, a known test in the art, a confusion matrix is used (Hand D J. 1981). A confusion matrix shows the probability that a response to a presented stimulus will be decoded as that stimulus. The vertical axis of the matrix gives the presented stimulus (i), and the horizontal axis gives the decoded stimulus (j). The matrix element at position (i,j) gives the probability that stimulus i is decoded as stimulus j. If j=i, the stimulus is decoded correctly, otherwise, the stimulus is decoded incorrectly. Put simply, elements on the diagonal indicate correct decoding; elements off the diagonal indicate confusion.

In this task, an array of stimuli is presented, specifically, stimuli containing natural sounds, and the extent to which the stimuli can be distinguished from each other, based on the responses of the SG cells and/or encoders, is measured.

A training set is obtained in order to build response distributions (the “training set”), and another set is obtained to be decoded to calculate the confusion matrix (the “test set”).

To decode the responses in the test set, one determines which of the stimuli s_(j) was the most likely to produce it. That is, one determines the stimulus s_(j) for which p(r|s_(j)) was maximal. Bayes theorem is used, which states that p(s_(j)|r)=p(r|s_(j))p(s_(j))/p(r), where p(s_(j)|r) is the probability that the stimulus s_(j) was present, given a particular response r; p(r|s_(j)) is the probability of obtaining a particular response r given the stimulus s_(j); and p(s_(j)) is the probability that the stimulus s_(j) was present. p(s_(j)) is set equal for all stimuli in this experiment and so, by Bayes Theorem, p(s|r_(j)) is maximized when p(r|s_(j)) is maximized. When p(s_(j)) is uniform, as it is here, this method of finding the most likely stimulus given a response is referred to as maximum likelihood decoding (Kass et al. 2005; Pandarinath et al. 2010; Jacobs et al. 2009). For each presentation of stimulus s_(i) that resulted in a response r that was decoded as the stimulus s_(j), the entry at position (i,j) in the confusion matrix is incremented.

To build the response distributions needed for the decoding calculations used to make the confusion matrices (i.e., to specify p(r|s_(j)) for any response r), the procedure is as follows. The response r is taken to be the spike train spanning 100 ms after stimulus onset and binned with 5 ms bins; this is the appropriate timescale in particular for speech sounds. The spike generation process is assumed to be an inhomogeneous Poisson process, and the probability p(r|s_(j)) for the entire 100 ms response is calculated as the product of the probabilities for each 5 ms bin. The probability assigned to each bin is determined by Poisson statistics, based on the average training set response in this bin to the stimulus s_(j). Specifically, if the number of spikes of the response r in this bin is n, and the average number of spikes in the training set responses in this bin is h, then the probability assigned to this bin is (h^(n)/n!)exp(−h). The product of these probabilities, one for each bin, specifies the response distributions for the decoding calculations used to make the confusion matrices.

Once the confusion matrices are calculated, overall performance in the forced choice visual discrimination task is quantified by “fraction correct”, which is the fraction of times over the whole task that the decoded responses correctly identified the stimuli. The fraction correct is the mean of the diagonal of the confusion matrix.

Given this procedure, at east sets of analyses may be performed. For each one, the responses from the normal SG cells are used for the training set and a different set of responses is used for the test set, as outlined below.

(1) The first set may include or consist of responses from normal SG cells. This is done to obtain the fraction correct produced by normal SG cells.

(2) The second set may include or consist of the responses from the encoders (in various embodiments, the responses from the encoders, as indicated throughout this document and that of the original application, may be streams of electrical pulses, e.g., spanning 100 ms after stimulus presentation, and binned with 5 ms, as are the normal SG responses). In other embodiments, other suitable durations and bin times may be used.

Responses from this test set yield a measure of how well the encoders perform, given the response distributions of the normal SG cells. The basis for this is that the brain is built to interpret the responses of the normal SG cells (i.e., the naturally encoded responses.) When responses from the encoder are used as a test set, one obtains a measure of how well the brain would do with our proxy of the normal SG responses (our proxy of the SG code).

(3) The third set may include or consist of responses from the SG cells of a deaf animal or human driven by the encoders and output generator (e.g., driving a ChR2 based transducer), where the responses are of the same duration and bin size as above. This set provides a measure of how well the encoder performs after its output has been passed through to real tissue.

As shown in Example 8 of the Retinal Application, the encoder's performance in the forced choice discrimination task was 98.75% of the normal retina's performance, and complete system's performance, that is, the performance of an embodiment of the encoder, output generator, and related transducer was 80% of the normal retina's performance. Thus, for various embodiments, when tested in vitro or in an animal or human model, the performance of the auditory prosthesis in the forced choice discrimination task, as measured by “fraction correct”, should be similar, that is at least about 35%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, or 100% of the performance of the normal SG cells, or better than the normal SG cells, measured as described above. Moreover, these performance levels may be obtained with response time scales for the prosthetic 200 which are substantially the same as those found in an unimpaired subject. That is, in some embodiments, the prosthetic 200 in jumping impaired cochlear hair cells introduces a lag time of which is of suitably short duration. For example, in some embodiments the lag time is less than a factor of 5, 4, 3, 2, 1, 0.5, 0.1, (e.g., a factor in the range of 0.1-5 or any subrange thereof) or less times the signaling time exhibited by a normal subject.

Other Embodiments

Although several examples have been provided, it is to be understood that numerous variations are within the scope of the present disclosure. For example, although prostheses for auditory and motor applications have been provided, it is to be understood that the devices and techniques may be applied in a variety of additional settings. Further, although various examples of cell and tissue types have been provided (e.g. jumping from SMA to SMN or muscle, or jumping from an audio stimulus to SG), it is to be understood that other types of cells, tissue, etc. may be used. In general, the devices and techniques described herein may be adapted to a wide variety of cases where a prosthetic is required which operates as a proxy for signaling cells which have suffered some form of gap or impairment.

Tables 2-6 summarize a number of applications where the devices and techniques described herein may be used to restore or improve function. For each application, the tables set forth a region of the nervous system that is impaired, the resulting body parts that have diminished function, the cause of the injury or impairment, the region from which activity is read (corresponding to A in FIG. 1B), the region which is stimulated (corresponding to C in FIG. 1B), and the connection that us bypassed or “jumped.” It is to be understood that the examples provided in the tables are in no way exhaustive.

TABLE 2 Body Part(s) Region of CNS That Have That is Diminished Cause of Impaired Function Impairment Input Region Output Region Connection dorsal column impairment of tabes dorsalis dorsal root (medial primary somatic tract aka procrioception, (sensory ataxia); or: right before lemniscus) VPL sensory cortex posterior/dorsal vibratory stereoanethesia lesioned part of thalamus (postcentral horn (dorsal sensation/loss (impaired tract; or: right after gyrus); white column) of deep tendon graphesia and Adams' and lesioned part of Waxman, 56; fibers reflex (skin, tactile Victor's tract; http://www.ncbi joints,tendons);i localization); Neurology Ch 9 Waxman 57; .nlm.nih.gov/bo mpaired two- multiple http://www.ncbi oks/NBK11142/ point sclerosis, .nlm.nih.gov/bo ;; Brazis 287 discrimination; vitamin B12 oks/NBK11142/ figure writing; deficiency, HIV detection of and human T- size, shape, lymphotropic weight, and virus infection; texture of Waxman 68; objects; ability Ropper and to detect the Samuels ch 9; direction and speed of a moving stimulus on the skin; Waxman 68,55,56,57; Ropper and Samuels Ch 9 Spinothalamic (skin) loss of Syringomyelia; posterior root (medial primary somatic Tract pain/temp stroke; trauma; ganglion axons lemniscus) VPL sensory cortex (ventrolateral sensation Waxman 66, 68; aka dorsal root thalamus (postcentral column fibers) below/opposite Kierman 77; or: right before or: right after gyrus); side of lesion http://www.ncbi lesioned part of lesioned part of Waxman 57; (ipsilateral lower .nlm.nih.gov/pm tract; tract; http://www.ncbi extremities) c/articles/PMC2 Snell 142; Waxman 56; .nlm.nih.gov/bo ;motor 170182/?tool=p Waxman Ch 5 http://www.ncbi oks/NBK10967/; weakness same ubmed; Sec III; .nlm.nih.gov/bo Brazis 287 side of lesion; http://www.ncbi oks/NBK10967/; ipsilateral side .nlm.nih.gov/pu Brazis 287 of face; bmed/14663044 neuronal hyperexcitability at injury/above the injury; pain below the injury level (central pain); Waxman 56;http://www. ncbi.nlm.nih.gov /books/NBK109 67/ ; Brazis 370; http://www.ncbi .nlm.nih.gov/pm c/articles/PMC2 170182/?tool=p ubmed;www.nc bi.nlm.nih.gov/p ubmed/1466304 4 Dorsal muscle X-Chromosome posterior root precerebellar inferior Spinocerebellar spindles/Golgi Linked Copper aka dorsal root nuclei cerebellar tract aka tendon Malabsorption ; (posterior gray or: right after peduncle; posterior organs,touch+pr Hereditory column) lesioned part of Kierman 72,167; spinocerebellar essure receptors Spastic or: right before tract; 2) tract (lateral via nucleus Paresis;spinocer lesioned part of Kierman 93; http://www.acc column fibers) dorsalis; ebellar ataxia tract; http://www.dart essmedicine.co ipsilateral lower (atrophy + Snell 147 mouth.edu/^(~)rsw m/content.aspx extremities demyelinization enson/NeuroSci ?alD=5272458& (vibration/positi of fibers); Miller /chapter_7A.ht searchStr=cereb onal sensory Fisher-Guillain ml ellar+peduncle# functions) Barre Overlap 5272458 ;severe Syndrome; ophthalmoplegi http://onlinelibr a (paralysis of ary.wiley.com/d eye muscles), oi/10.1002/ana. bilateral 410050609/a bst ptosis(eyelid), ract; areflexia, http://www.scie and moderate ncedirect.com/s cerebellar cience/article/pi ataxia; i/0022510X9490 irresponsive 037X; (SCA 2) pupils; facial http://www.ncbi nerve palsy; .nlm.nih.gov/pu Waxman 56; bmed/14507334 Brazis 370 ; ; http://cornell.w http://www.ncbi orldcat.org/title/ .nlm.nih.gov/pu position-and- bmed/7876862 vibration- sensations- functions-of- the-dorsal- spinocerebellar- tracts/ocic/1147 67304&referer= brief_results; http://www.ncbi .nlm.nih.gov/pu bmed/7876862 Ventral 1)muscle a) Miller Fisher- dorsal root precerebellar superior Spinocerebellar spindles/Golgi Guillain Barre ganglion axons nuclei cerebellar tract aka tendon organs Overlap (posterior gray or: right after peduncle ((to anterior (sensory input Syndrome; column) lesioned part of cerebellar spinocerebellar from skeletal b) Friedrich's or: right before tract; cortex) ; tract muscle) Syndrome lesioned part of Kierman 93 Kierman 72; ,touch+pressure (heredo-ataxia); tract; ;http://www.dar http://www.acc receptors; 2) a) Snell 146 tmouth.edu/^(~)rs essmedicine.co severe http://www.ncbi wenson/NeuroS m/content.aspx ophthalmoplegi .nlm.nih.gov/pu ci/chapter_7A.ht ?alD=5272458& a (paralysis of bmed/7876862; ml searchStr=cereb eye muscles), b) ellar+peduncle# bilateral http://www.ncbi 5272458 ptosis(eyelid), .nlm.nih.gov/pu areflexia, bmed?term=%2 and moderate 2ventral%20spin cerebellar ocerebellar%20t ataxia; ract%22%20atax irresponsive ia pupils; 3) facial nerve palsy; optic nerve degeneration; 1) Waxman 56; http://www.blac kwellpublishing. com/patestas/c hapters/10.pdf; 2) http://www.ncbi .nlm.nih.gov/pu bmed/7876862; 3) http://www.ncbi .nlm.nih.gov/pu bmed?term=%2 2ventral%20spin ocerebellar%20t ract%22%20atax ia Spinoreticular deep somatic Wallenberg's posterior root reticular Thalamus; Tract (lateral structures ; lack Syndrome; ganglion axons formation cerebral cortex; column) of triggering of http://www.ncbi or: right before (precerebellar http://www.blac noxious .nlm.nih.gov/pu lesioned part of nucleus) kwellpublishing. inhibitory bmed?term=Diff tract; or: right after com/patestas/c controls use%20noxious Snell 150 lesioned part of hapters/10.pdf ; (nonpainful but %20inhibitory% tract; Latash 171 noxious stimuli) 20controls%20in http://www.acc (Neurophysiolog ; hemianalgesia; %20man.%20Inv essmedicine.co ical basis of Waxman 56; olvement%20of m/content.aspx movement) http://www.ncbi %20the%20spin alD=5271956& .nlm.nih.gov/pu oreticular%20tra searchStr=spino bmed?term=Diff ct cerebellar+tract use%20noxious s#5271956. %20inhibitory% Kierman 72; 20controls%20in http://www.scie %20man.%201nv ncedirect.com/s olvement%20of cience/article/pi %20the%20spin i/S03010082980 oreticular%20tra 00483 ct Corticopontocer myelin decay ataxic nerve cells in pontine nuclei cerebellar ebellar Pathway (white matter neurodegerenati frontal/parietal/ or: right after cortex; (pontocerebellar tracts); ve diseases temporal/occipit lesioned part of Snell 226-229 tract; pontine dysarthria (hereditary al lobes of tract; nuclei; part of (mouth), spinocerebellar cerebral cortex Snell 226-229 cerebellum) hemiparesis of ataxia); multiple or: right before one side, system atrophy lesioned part of nystagmus (MSA); late- tract; (involuntary eye onset cerebellar Snell 226-227 twitching); cortical atrophy http://www.ncbi (LCCA); stroke; .nlm.nih.gov/pu http://www.ncbi bmed/18172629 .nlm.nih.gov/pu bmed/18172629 http://www.ncbi .nlm.nih.gov/pu bmed/8342190 cerebro- involuntary eye olivocerebellar nerve cells in inferior olivary cerebellar olivocerebellar twitching atrophy; frontal/parietal/ nuclei cortex; tract fibers (rebound cerebellar temporal/occipit or: right after Snell 229 nystagmus), ataxia; al lobes of lesioned part of wasting of small http://www.ncbi cerebral cortex tract; muscles of both .nlm.nih.gov/pu or: right before Snell 229 hands, spastic bmed/7931442; lesioned part of paralysis of both tract; legs, Snell 226 dysdiadochokine sia (lack of coordination)of upper limbs, ocular dysmetria; http://www.ncbi .nlm.nih.gov/pu bmed/7931442;

TABLE 3 Body Part(s) Region of CNS That Have That is Diminished Cause of Impaired Function Impairment Input Region Output Region Connection Ventromedial ipsilateral Kierman 1) dorsomedial medial inferior medulla hypoglossal 108,109; hypothalamus lemniscus; cerebellar palsy (tongue Head and neck neurons Waxman 86 peduncle paralysis); surgery-- 2) midbrain (cerebral contralateral otolaryngology periaqueductal cortex); hemiplegia/hem By Byron J. gray (for rostral Waxman 86; iparesis; loss of Bailey p119; ventromedial Smith et al 45 sense of Jonas T. medulla); temp/pain Johnson, Shawn 1) (skin); D. Newlands; http://www.ncbi Kierman 108; http://stroke.ah .nlm.nih.gov/pu http://www.scie ajournals.org/co bmed/21196160 ncedirect.com/s ntent/26/4/702. 2) cience/article/pi full; http://www.ann i/S10523057988 http://www.scie ualreviews.org/ 0027X; ncedirect.com/s doi/abs/10.1146 http://www.nej cience/article/pi /annurev.ne.14. m.org/doi/full/1 i/S03064522060 030191.001251 0.1056/ENEJMic 03836; m020058; http://www.scie http://www.scie ncedirect.com/s ncedirect.com/s cience/article/pi cience/article/pi i/S10523057988 i/S10523057988 0027X 0027X; http://archneur. ama- assn.org/cgi/rep rint/57/4/478 Lateral Medulla 1. ipsilateral Wallenberg's solitary tract medial inferior palate paralysis syndrome nucleus (NTS) ; lemniscus; cerebellar (roof of mouth); “Lateral http://www.scie Waxman 86 peduncle vocal cord Medullary ncedirect.com/s (cerebral paralysis; loss of Syndrome” cience/article/pi cortex); pain/heat (vertigo, ataxia); i/S10538119080 Waxman 86; sensation on caused by 1001X; Smith et al 45 same side of inferior artery http://onlinelibr face/opposite of occlusion; ary.wiley.com/d body (skin?); trauma; stroke; oi/10.1002/cne. loss of facial Kierman 107, 21105/abstract sweating (skin); 108,109; 2. diminishment http://www.spri pf pharyngeal ngerlink.com/co reflex (pharynx); ntent/p7662218 limb weakness; 4414hr60/ ; Kierman Waxman Ch 7 108,110; Clinical http://keur.eldo Illustration 7-1 c.ub.rug.nl/FILES /wetenschapper s/1/478/478.pdf ; http://stroke.ah ajournals.org/co ntent/28/4/809. abstract; Brazis 369; http://www.spri ngerlink.com/co ntent/f3488893 72351m38/ Lateral Medulla ipsilateral plate Avellis' solitary tract medial inferior paralysis; vocal syndrome; nucleus (NTS); lemniscus; cerebellar cord paralysis; dysphagia; acute http://www.scie Waxman 86 peduncle loss of pain/heat stroke; Kierman 108; ncedirect.com/s (cerebral on ipsilateral http://www.ncbi cience/article/pi cortex); side of .nlm.nih.gov/pu i/S10538119080 Waxman 86; face/contralater bmed/8821503; 1001X; Smith et al 45 al side of body http://www.ncbi http://onlinelibr (face-arm-trunk- .nlm.nih.gov/pu ary.wiley.com/d legs); bmed/21576937 oi/10.1002/cne. Kierman 108; 21105/abstract http://www.ncbi .nlm.nih.gov/pm c/articles/PMC2 170182/pdf/v06 5p00255.pdf ; http://jnnp.bmj. com/content/65 /2/255.abstract pons ipsilateral LMN Millard Gubler's midbrain basis pontine nuclei; middle (corticospinal paralysis syndrome; pedunculi Kierman 101; cerebellar fibers/descendin (face);contralate trauma; or: undamaged Smith 56 peduncle g fibers) ral hemiplesia; Kierman part of fibers (cerebellum); or: undamaged 108;localization right after Kierrman 101 fibers before the in clinical lesion; Wxman 89; lesion; neurology by Kierman 101 Brazis et al 357 Kierman 108 ; braxis/masdeu/ localization in biller 291; clinical http://content.k neurology by arger.com/Prod braxis/masdeu/ ukteDB/produkt biller 291,553 ; e.asp?Aktion=Sh http://content.k owPDF&ArtikelN arger.com/Prod r=000116965&A ukteDB/produkt usgabe=234289 e.asp?Aktion=Sh &ProduktNr=22 owPDF&ArtikelN 3840&filename= r=000116965&A 000116965.pdf usgabe=234289 &ProduktNr=22 3840&filename= 000116965.pdf dorsal pons ipsilateral LMN Foville's pontine nuclei; middle (pontine facial paralysis Syndrome Kierman 101; cerebellar tegmentum) (face); ipsilateral (lower dorsal Smith 56 peduncle conjugate gaze pontine (cerebellum); paralysis (eyes); syndrome) ; Kierrman 101; contralateral Wall-Eyed Waxman 89; hemiparesis; Internuclear Brazis et al 357 blepharospasm Ohtalmoplegia (eyelid closing); WEBINO motor tract syndrome damage; facial (caused by nerve damage; stroke; multiple failure to abduct schlerosis; eye; infections); Kierman stroke ; 108,110; Kierman http://keur.eldo 108,110,111,121 c.ub.rug.nl/FILES ; /wetenschapper http://stroke.ah s/1/478/478.pdf ajournals.org/co ; ntent/11/1/84.a http://stroke.ah bstract; Brazis et ajournals.org/co al 359; http://www.spri ntent/28/4/809. ngerlink.com/co abstract; ntent/f3488893 Brazis 369; 72351m38/ ; http://www.spri http://www.ncbi ngerlink.com/co .nlm.nih.gov/pu ntent/f3488893 bmed/21729278 72351m38/ ventral pons ipsilateral Raymond's pontine nuclei middle (pontocerebellar abducens nerve Syndrome; neurons' axons cerebellar fibers) palsy (VI nerve, Locked in (cerebral cortex) peduncle lateral rectus Syndrome or: undamaged (cerebellum) muscle of eye); (caused by portion of or: undamaged contralateral Stroke or pontocerebellar portion of hemiparesis (1/2 traumatic brain fibers right pontocerebellar of body);upper injury due to before the fibers right after motor neuron obstructed the lesion; Kierman 101 quadriplegia, basilar artery); lesion; Kierman 101; Waxman 89; paralysis of anarthria Smith 56 Brazis et al. 357 lower cranial (speech loss); nerves, quadriplegia; bilateral paresis Kierman 108; of horizontal http://www.spri gaze; ngerlink.com.pr Kierman 108 ; oxy.library.corne http://stroke.ah II.edu/content/7 ajournals.org/co 4n878271705ru ntent/28/4/809. 11/; abstract; http://www.ncbi http://www.ncbi .nlm.nih.gov/pu .nlm.nih.gov/pu bmed/12119076 bmed/12119076 cerebral ipsilateral Benedikt's lenticular Pontine Nuclei; medulla peduncle ( oculomotor Syndrome; nucleus aka Kahle and oblongata; pyramidal abducens nerve Weber's corpus striatum Frotscher 166; Morris and fibers/fascicle of palsy (pupil of syndrome (i.e. externus Morris and McMurrich 876 cranial nerve 3 ) eyes); Ventral (olfactory lobe McMurich 871 contralateral Midbrain fasciculi) hemiparesis;con Syndrome); or: unlesioned tralateral peduncular part hemiplegia hallucinosis (for Stricker 416 (face); tremor + vascular involuntary lesions); stroke ; movements (red Dysarthia nucleus (Clumsy Hand destruction); Syndrome); heaviness of Kierman 108; limbs/difficulty http://www.harr using isonspractice.co hand/slurred m/practice/ub/v speech (disorder iew/Harrisons% of articulatory 20Practice/1416 movements of 11/all/Double+30V tongue + oris ision;http://ww muscles); w.ncbi.nlm.nih.g unwanted hand ov/pubmed/188 activity ; 26349 Kierman 108; http://www.brig http://onlinelibr hamandwomens ary.wiley.com/d .org/Departmen oi/10.1002/mds. is and Services 10084/full; /neurology/servi http://www.ncbi ces/NeuroOphth .nlm.nih.gov/pu amology/Images bmed/18826349 /SelectedPublica ; tions/Strabismu http://www.brig s.pdf ; hamandwomens Brazis 361, 362; .org/Departmen http://www.ncbi ts_and_Services .nlm.nih.gov/pu /neurology/servi bmed?term=Gel ces/NeuroOphth ler%20TJ%2C%2 amology/Images OBellur%20SN.% /SelectedPublica 20Peduncular%2 tions/Strabismu Ohallucinosis%3 s.pdf; A%20magnetic% http://www.scie 20resonance%2 ncedirect.com/s Oimaging%20co cience/article/pi nfirmation%20of i/S08872171019 %20mesenceph 00034 ; alic%20infarctio Brazis 361, 362; n%20during%20l Theime's ife.%20Ann%20 Anatomic Basis Neurol%201987 of Neurologic %3B21%3A602% Diagnosis Atlas E2%80%93604; 226; http://www.ncbi http://www.ncbi .nlm.nih.gov/pu .nlm.nih.gov/pm bmed/17621531 c/articles/PMC1 http://www.scie 073816/ ncedirect.com/s cience/article/pi i/S10523057080 01535;http://w ww.ncbi.nlm.nih .gov/pmc/article s/PMC1073816/ dorsal midbrain conjugate Parinaud's occipital cortex a) caudal a) Thalamus; (superior upward gaze syndrome (aka (corticotectal nucleus ; b) primary visual colliculus, paralysis w-o dorsal midbrain fibers); b) lateral cortex; pretectal area, paralysis of syndrome, Kierman 102- geniculate Westerlain 248; posterior convergence; pretectal 103 nucleus (LGN); http://www.ncbi commisure etc) abnormalities of syndrome, http://www.ncbi .nlm.nih.gov/pu pupil response Sylvian .nlm.nih.gov/pu bmed/21344403 (eyes); paralysis aqueduct bmed/21344403 of vertical gaze; syndrome) ; ipsilateral head tumor pressure tilt; vertical on posterior diplopia commissure/pre (downwards/co tectal ntralesional area/superior gaze); colliculi ; Kierman 108, trauma; stroke; 121; Horner's http://www.ncbi Syndrome; .nlm.nih.gov/pu miningitis/herpe bmed/20182210 s zoster/syphilis ; (connective http://www.scie tissue ncedirect.com/s infections); cience/article/pi Kierman i/S03038467090 108,121; 02406; http://stroke.ah ajournals.org/co ntent/12/2/251. abstract; http://www.ncbi .nlm.nih.gov/pu bmed/20182210 ; http://www.scie ncedirect.com/s cience/article/pi i/S03038467090 02406 Middle ipsilateral facial anterior inferior pontocerebellar Cerebellum; cerebellar paralysis (face), cerebellar artery fibers (from Young et al 105 peduncle aka impaired facial (AICA) injury; pontine nuclei branchial pontis sensation (skin); lateral inferior neurons' axons); paralysis of pontine Kierman 101 conjugate gaze syndrome; to the side of ataxia; the lesion aneurysm; (eyes); stroke ; cardiac contralateral embolism; sense loss of trauma; temp/pain; localization in deafness (ears) ; clinical tinnitus (ears); neurology by middle braxis/masdeu/ cerebellar biller 553; peduncle http://www.ncbi infarction .nlm.nih.gov/pu (nystagmus, bmed/21748288 speech ; difficulty, ataxia http://www.ncbi of limbs/trunk); .nlm.nih.gov/pu inner ear bmed/20572906 dysfunction ;http://www.nc (vertigo/tinnitus bi.nlm.nih.gov/p /bilateral ubmed/1983486 hearing loss); 5; (anterior inferior http://www.ncbi cerebellar .nlm.nih.gov/pu artery-related) bmed/21631321 localization in clinical neurology by braxis/masdeu/ biller 553; http://stroke.ah ajournals.org/co ntent/33/12/28 07.full; http://brain.oxf ordjournals.org/ content/113/1/1 39.abstract?ijke y=60f163a9bdc3 3efe496746bc1e ffc8f9c4e1dd9c &keytype2=tf_ip secsha; http://www.ncbi .nlm.nih.gov/pu bmed/20572906 ;http://www.nc bi.nlm.nih.gov/p ubmed/1983486 5;http://www.n cbi.nlm.nih.gov/ pubmed/197971 77

TABLE 4 Body Part(s) Region of CNS That Have That is Diminished Cause of Impaired Function Impairment Input Region Output Region Connection C2 root impairment of tumors ; dorsal spinothalamic periaquaductal respiratory http://www.ncbi rami/afferent tract, grey (midbrain); function; .nlm.nih.gov/pu fibers from spinomesenceph thalamus; Currrent bmed/21123996 dorsal root alic tract; http://www.ncbi Treatment and ganglion http://www.ncbi .nlm.nih.gov/pu Diagnostic in or: right before .nlm.nih.gov/pu bmed/2358537 Orthopedics ch the lesioned part bmed/2358537 (for cervical 13 of the root; (for cervical enlargement Kierman 62; enlargement projections in Waxman 48-49 projections in general); general); Thieme Color Thieme Color Atlas of the Atlas of the Human Body, Human Body, p558- p558- 559(reference 559(reference for cervical for cervical enlargement enlargement components) components) C3 root jaw/neck; sensory dorsal 1) Ventral 1) cerebellum infrahyoids, disturbances; rami/afferent Spinocerebellar 2) semispinalis muscle paresis; fibers from Tract; periaquaductal capitis and (trauma) dorsal root 2) spinothalamic grey (midbrain); cervicis, subluxation of ganglion tract, thalamus; longissimus spinal axis; or: right before spinomesenceph 1) capitis and degenerative the lesioned part alic tract; http://www.ncbi cervicis, motor root C3 of the root; 1) .nlm.nih.gov/pu intertransversarii compression Kierman 62; http://www.ncbi bmed/14337566 , rotatores, (ventral osseus Waxman 48-49 .nlm.nih.gov/pu ?dopt=Abstract& multifidi muscle compression); bmed/14337566 holding=npg ; paresis;diapragm Brazis et al 93; ?dopt=Abstract& http://www.ncbi weakness http://www.ncbi holding=npg; .nlm.nih.gov/pu /anterior trunk; .nlm.nih.gov/pu 2)http://www.nc bmed/2358537 Brazis et al 93; bmed/21120549 bi.nlm.nih.gov/p (for cervical waxman 51 ubmed/2358537 enlargement (for cervical projections in enlargement general); projections in Thieme Color general); Atlas of the Thieme Color Human Body, Atlas of the p558- Human Body, 559(reference p558- for cervical 559(reference enlargement for cervical components); enlargement 2) components) http://www.scie ncedirect.com/s cience/article/pii /S000689939800 4120 (for cuneate nucleus in general); Thieme Color Atlas of Human Anatomy Vol III - Nervous System and Sensory Organs 341 (for cuneate nucleus- thalamus-cortex connection) Thieme Color Atlas of the Human Body, p558- 559(reference for cervical enlargement components) C4 root scalene/levator muscle paresis; dorsal spinothalamic 1) scapulae/trapez degenerative rami/afferent tract, periaquaductal oid motor root C4 fibers from spinomesenceph grey (midbrain); (shoulder)/rhom compression dorsal root alic tract; thalamus boid muscles (ventral osseus ganglion 1) 2) postcentral paresis; compression); or: right before http://www.ncbi cyrus (from diaphragmic trauma/birth the lesioned part .nlm.nih.gov/pu cuneate nucleus paresis + injury;trauma; of the root; bmed/14337566 to thalamus); pulmonary compression by Kierman 62; ?dopt=Abstract& 1) difficulty;diaphra a ganglion; Waxman 48-49 holding=npg; http://www.ncbi gm Brazis et al 93; 2)http://www.nc .nlm.nih.gov/pu weakness/anteri http://www.ncbi bi.nlm.nih.gov/p bmed/14337566 or trunk; .nlm.nih.gov/pu ubmed/2358537 ?dopt=Abstract& Brazis et al 93; bmed/21120549 (for cervical holding=npg ; waxman 51 enlargement http://www.ncbi projections in .nlm.nih.gov/pu general); bmed/2358537 Thieme Color (for cervical Atlas of the enlargement Human Body, projections in p558- general); 559(reference Thieme Color for cervical Atlas of the enlargement Human Body, components) p558- 559(reference for cervical enlargement components); 2) http://www.scie ncedirect.com/s cience/article/pii /S000689939800 4120 (for cuneate nucleus in general); Thieme Color Atlas of Human Anatomy Vol III - Nervous System and Sensory Organs 341 (for cuneate nucleus- thalamus-cortex connection) Thieme Color Atlas of the Human Body, p558- 559(reference for cervical enlargement components) C5 root neck/shoulder/u depressed bicets dorsal spinothalamic 1) pper anterior reflex; rami/afferent tract, periaquaductal arm pain; depressed fibers from spinomesenceph grey (midbrain); sensory brachioradialis dorsal root alic tract; thalamus disturbances on reflex;cervical ganglion http://www.ncbi 2) postcentral lateral arm; spondylosis(deg or: right before .nlm.nih.gov/pu cyrus (from muscle paresis enerative tissue the lesioned part bmed/2358537 cuneate nucleus for levator of cervical of the root; (for cervical to thalamus); scapulae, vertebrae); Kierman 62; enlargement http://www.ncbi rhomboids, cervical Waxman 48-49 projections in .nlm.nih.gov/pu serratus radiculopathy; general); bmed/2358537 anterior, post-operative Thieme Color (for cervical supraspinatus, (decompression/ Atlas of the enlargement infraspinatus, spinal cord Human Body, projections in deltoid, biceps, fusion) C5 palsy p558- general); brachioradialis; following 559(reference Thieme Color diaphragmatic anterior for cervical Atlas of the paresis (if decompression enlargement Human Body, damaged C5 and spinal fusion components); p558- fibers reach for cervical http://www.scie 559(reference phrenic nerve); degenerative ncedirect.com/s for cervical biceps/brachiora diseases; cience/article/pii enlargement dialis (poor contributing pre- /S000689939800 components) reflex); existing 4120 hemidiaphragmi asymptomatic c paresis +30 damage of the pulmonary anterior horn difficulty; cells at C3-C4 radicular pain and C4-C5 levels (suprascaular (motor region of weakness); root);deltoid upper brachial weakness; plexus palsies; Brazis et al 93; high velocity http://www.joso impact (like nline.org/abstrac football) causing ts/v18n3/356.ht nerve avulsion; ml; Waxman 51 trauma; compression by a ganglion; Brazis et al 93; Frank 1031; http://www.joso nline.org/abstrac ts/v18n3/356.ht ml; http://www.ncbi .nlm.nih.gov/pu bmed/20461418 ; http://www.scie ncedirect.com/s cience/article/pii /S036350231000 5101; http://www.ncbi .nlm.nih.gov/pm c/articles/PMC2 504282/ ; Theime Atlas of Neurology 767- 768 C6 root lateral/dorsal hyperflexia (if dorsal 1) n/a 1) Ventrolateral forearm pain; corticalspinal rami/afferent 2) Spinothalamic Medulla (VLM) paresis of tract is damage); fibers from tract (dorsal Nuclei; Solitary muscles (erratus depressed dorsal root column); tract nucleus anterior, biceps, biceps/brachiora ganglion 1) n/a (NTS), lateral pronator teres, dialis reflex (due or: right before 2) reticular nucleus flexor carpi to compression the lesioned part http://www.scie (LRt), radialis, of C5-6 vertebral of the root; ncedirect.com/s caudal/rostral brachioradialis, level); cervical Kierman 62; cience/article/pii ventrolateral extensor carpi spondylosis(deg Waxman 48-49 /S000689939800 medulla; radialis longus, enerative tissue 4120 2) postcentral supinator, and of cervical cyrus (from extensor carpi vertebrae); cuneate nucleus radialis brevis); cervical to thalamus); depressed radiculopathy;up 1) biceps/brachiora per brachial http://www.scie dialis reflex; plexus palsies; ncedirect.com/s radicular pain ipsilateral root cience/article/pii (posterior injury;C5-C6 /S156607020200 deltoid unilateral facet 0346; region);biceps dislocation 2) weakness; (vertebrae injury http://www.scie Brazis et al 93; due to trauma ncedirect.com/s http://www.joso like car cience/article/pii nline.org/abstrac accident); high /S000689939800 ts/v18n3/356.ht velocity impact 4120 (for ml; (football) cuneate nucleus Waxman 51 causing nerve in general); avulsion; Thieme Color trauma; Atlas of Human compression by Anatomy Vol III - a ganglion; Nervous System Brazis et al 93; and Sensory Frank 1031; Organs 341 (for http://www.joso cuneate nucleus- nline.org/abstrac thalamus-cortex ts/v18n3/356.ht connection) ml; http://www.scie ncedirect.com/s cience/article/pii /S036350231000 5101; Johnson Ch 29 (Principles of Critical Care); http://www.ncbi .nlm.nih.gov/pm c/articles/PMC2 504282/ C7 root pain in dorsal compression due dorsal 1) n/a 1) Ventrolateral forearm/deep to disc rami/afferent Medulla (VLM) breast; sensory herniation at C6- fibers from Nuclei; Solitary disturbances on 7 vertebral level; dorsal root tract nucleus 3rd/4th digits; cervical ganglion (NTS), lateral paresis of osteoarthritis;ce or: right before reticular nucleus muscles ( rvical the lesioned part (LRt), serratus spondylosis of the root; caudal/rostral anterior, (degenerative Kierman 62; ventrolateral pectoralis major, tissue of cervical Waxman 48-49 medulla; latissimus dorsi, vertebrae); 1) pronator teres, cervical http://www.scie flexor carpi radiculopathy ncedirect.com/s radialis, triceps, ;upper brachial cience/article/pii extensor carpi plexus palsies; /S156607020200 radialis longus, trauma; 0346; extensor carpi Brazis et al 94; brevis,extensor Frank 1031; digitorum); http://www.joso triceps reflex nline.org/abstrac depressed; ts/v18n3/356.ht pseudomyotonia ml; of hand http://www.scie (difficulty in ncedirect.com/s opening b/c of cience/article/pii cervical /S036350231000 osteoarthritis); 5101 ; radicular pain Thieme Atlas of (interscapular Neurology 768 region) ;triceps weakness; Brazis et al 94; http://www.joso nline.org/abstrac ts/v18n3/356.ht ml; Waxman 51 C8 root pain in the compression due dorsal 1) n/a 1) Ventrolateral medial to disc rami/afferent Medulla (VLM) arm/forearm; herniation at C7- fibers from Nuclei; Solitary fifth digit; medial T1 vertebral dorsal root tract nucleus forearm/hand; level; ipsilateral ganglion (NTS), lateral paresis of Horner or: right before reticular nucleus muscles (flexor Syndrome; the lesioned part (LRt), digitorum cervical of the root; caudal/rostral superficialis, radiculopathy; Kierman 62; ventrolateral flexor pollicis nerve root Waxman 48-49 medulla; longus, flexor blockage ; 1) digitorum trauma/birth http://www.scie profundus Ito trauma; tumor ncedirect.com/s IV, pronator of lung apex; cience/article/pii quadratus, ly,phomatous /S156607020200 abductor pollicis infiltration; 0346 brevis, opponens pressure lesion pollicis, flexor at elbow; pollicis brevis, all traumatic palsy lumbricals, (from a flexor carpi blow/knife/glass ulnaris, abductor at the wrist or digiti minimi, elbow fractures); opponens digiti delayed nerve minimi, flexor palsy (ages after digiti minimi, all an elbow interossei, fracture/dislocat adductor pollicis, ion etc +vagus extensor digiti deformity) ; minimi, extensor arthrosis; carpi ulnaris, Brazis et al 94; abductor pollicis http://www.joso longus, extensor nline.org/abstrac pollicis longus ts/v18n3/356.ht and brevis, and ml; extensor indicis); Thieme Atlas of depressed finger Neurology 753, flexor reflex; 759, 776 razis et al 94; http://www.joso nline.org/abstrac ts/v18n3/356.ht ml; Thieme Atlas of Neurology 779 radicular pain (interscapular/sc apular region of nerve root) ; motor deficit in hand muscles T3 root decreased arachnoid dorsal Dorsal cerebellum sensation of skin calcifications rami/afferent Spinocerebellar (cerebellar (dermatome); (caused by fibers from Tract; cortex); radicular trauma); dorsal root http://www.ncbi http://www.ncbi pain/low back myelography, ganglion .nlm.nih.gov/pu .nlm.nih.gov/pu pain/paralysis; subarachnoid or: right before bmed/14337566 bmed/14337566 Brazis et al 94; hemorrhage, the lesioned part ?dopt=Abstract& ?dopt=Abstract& http://www.ncbi spinal of the root; holding=npg holding=npg .nlm.nih.gov/pu anesthesia; Kierman 62; bmed/17149734 http://www.ncbi Waxman 48-49 .nlm.nih.gov/pu bmed/17149734 T4 root decreased intramecdullary dorsal Dorsal cerebellum sensation of skin tumor (spinal rami/afferent Spinocerebellar (cerebellar (dermatome);nu metastasis); fibers from Tract; cortex); mbness in arachnoid dorsal root http://www.ncbi http://www.ncbi body/both legs; calcifications ganglion .nlm.nih.gov/pu .nlm.nih.gov/pu bladder/bowel (caused by or: right before bmed/14337566 bmed/14337566 dysfunction; trauma); the lesioned part ?dopt=Abstract& ?dopt=Abstract& progressive subarachnoid of the root; holding=npg holding=npg weakness of hemorrhage; Kierman 62; bilateral lower http://www.ncbi Waxman 48-49 extremities; .nlm.nih.gov/pu radicular bmed/19398862 pain/low back ; pain/paralysis; http://www.ncbi Brazis et al 94; .nlm.nih.gov/pu http://www.ncbi bmed/17149734 .nlm.nih.gov/pu bmed/19398862 ; http://www.ncbi .nlm.nih.gov/pu bmed/17149734 ; http://www.ncbi .nlm.nih.gov/pu bmed/17149734 T5 root decreased T5 nerve root dorsal Dorsal cerebellum sensation of skin fistula; trauma rami/afferent Spinocerebellar (cerebellar (dermatome); (particularly fibers from Tract; cortex); Brazis et al 94; head injuries or dorsal root http://www.ncbi http://www.ncbi http://www.ncbi penetrating ganglion .nlm.nih.gov/pu .nlm.nih.gov/pu .nlm.nih.gov/pu damage to the or: right before bmed/14337566 bmed/14337566 bmed/8133999 spine) ; the lesioned part ?dopt=Abstract& ?dopt=Abstract& http://www.ncbi of the root; holding=npg holding=npg .nlm.nih.gov/pu Kierman 62; bmed/8133999 Waxman 48-49 T6 root decreased schwannoma dorsal Dorsal cerebellum sensation of skin (nerve sheath rami/afferent Spinocerebellar (cerebellar (dermatome); tumor); fibers from Tract; cortex); gait control http://www.scie dorsal root http://www.ncbi http://www.ncbi difficulty; tactile ncedirect.com/s ganglion .nlm.nih.gov/pu .nlm.nih.gov/pu hypaethesia cience/article/pii or: right before bmed/14337566 bmed/14337566 below T6; /S096758680600 the lesioned part ?dopt=Abstract& ?dopt=Abstract& Brazis et al 94; 2384 of the root; holding=npg holding=npg http://www.scie Kierman 62; ncedirect.com/s Waxman 48-49 cience/article/pii /S096758680600 2384 T7 root no movement in T6-T7 injury dorsal Dorsal cerebellum lower (trauma); rami/afferent Spinocerebellar (cerebellar extremities; gait schwannoma fibers from Tract; cortex); control difficulty; (tumor of nerve dorsal root http://www.ncbi http://www.ncbi thermic/algic sheath); ganglion .nlm.nih.gov/pu .nlm.nih.gov/pu hypaethesia arachnoid or: right before bmed/14337566 bmed/14337566 below T7; calcifications w/ the lesioned part ?dopt=Abstract& ?dopt=Abstract& http://www.ncbi possible of the root; holding=npg holding=npg .nlm.nih.gov/pu arachnoid Kierman 62; bmed/18662744 ossification + Waxman 48-49 ; nerve root http://www.scie compression ncedirect.com/s (caused by cience/article/pii trauma or /S096758680600 interspinal 2384 tumor); myelography, subarachnoid hemorrhage, spinal anethesia; http://www.ncbi .nlm.nih.gov/pu bmed/18662744 ; O'Rahilly et al ch 41; http://www.scie ncedirect.com/s cience/article/pii /S096758680600 2384 ; http://www.ncbi .nlm.nih.gov/pu bmed/17149734 T8 root no movement in T6-T7 injury dorsal Dorsal cerebellum lower (trauma) ; T7-T8 rami/afferent Spinocerebellar (cerebellar extremities; injury (trauma); fibers from Tract; cortex); complete T7 level injury dorsal root http://www.ncbi http://www.ncbi motor/sensory (trauma); ganglion .nlm.nih.gov/pu .nlm.nih.gov/pu deficit; lack of http://www.ncbi or: right before bmed/14337566 bmed/14337566 sphincter/sexual .nlm.nih.gov/pu the lesioned part ?dopt=Abstract& ?dopt=Abstract& function and bmed/18662744 of the root; holding=npg holding=npg control;radicular ; Kierman 62; pain/low back O'Rahilly et al ch Waxman 48-49 pain/paralysis; 41. http://www.ncbi .nlm.nih.gov/pu bmed/18662744 ; http://www.ncbi .nlm.nih.gov/pu bmed/17149734 T9 root lack of T7-T8 dorsal Dorsal cerebellum sphincter/sexual injury(trauma); rami/afferent Spinocerebellar (cerebellar function and arachnoid fibers from Tract; cortex); control; calcifications w/ dorsal root http://www.ncbi http://www.ncbi http://www.ncbi possible ganglion .nlm.nih.gov/pu .nlm.nih.gov/pu .nlm.nih.gov/pu arachnoid or: right before bmed/14337566 bmed/14337566 bmed/18662745 ossification + the lesioned part ?dopt=Abstract& ?dopt=Abstract& nerve root of the root; holding=npg holding=npg compression Kierman 62; (caused by Waxman 48-49 trauma); myelography, subarachnoid hemorrhage, spinal anesthesia; http://www.ncbi .nlm.nih.gov/pu bmed/18662744 ; http://www.ncbi .nlm.nih.gov/pu bmed/17149734 ; O'Rahilly et al ch 41 T10 root bilateral T9 Injury dorsal Dorsal cerebellum abdominal (trauma); tumor rami/afferent Spinocerebellar (cerebellar muscle paresis; pressure; fibers from Tract; cortex); trace http://www.ncbi dorsal root http://www.ncbi http://www.ncbi movements/hyp .nlm.nih.gov/pu ganglion .nlm.nih.gov/pu .nlm.nih.gov/pu oethesia (partial bmed/18662744 or: right before bmed/14337566 bmed/14337566 loss of ; the lesioned part ?dopt=Abstract& ?dopt=Abstract& sensation) in O'Rahilly et al ch of the root; holding=npg holding=npg lower 41. Kierman 62; extremities; lack Waxman 48-49 of sphincter/sexual function or control; muscle spasms; Brazis et al 94; http://www.ncbi .nlm.nih.gov/pu bmed/18662745 T11 root excessive lateral disc dorsal Dorsal cerebellum protrusion of herniation rami/afferent Spinocerebellar (cerebellar abdomen (when causing fibers from Tract; cortex); inspiring); compression on dorsal root http://www.ncbi http://www.ncbi bilateral root; ganglion .nlm.nih.gov/pu .nlm.nih.gov/pu abdominal http://www.ncbi or: right before bmed/14337566 bmed/14337566 muscle paresis; .nlm.nih.gov/pu the lesioned part ?dopt=Abstract& ?dopt=Abstract& Brazis et al 94 bmed/18090072 of the root; holding=npg holding=npg Kierman 62; Waxman 48-49 T12 root excessive trauma; nerve dorsal Dorsal cerebellum protrusion of root avulsion; rami/afferent Spinocerebellar (cerebellar abdomen (when associated fibers from Tract; cortex); inspiring); syringomyelia; dorsal root http://www.ncbi http://www.ncbi bilateral tumor pressure ; ganglion .nlm.nih.gov/pu .nlm.nih.gov/pu abdominal lateral disc or: right before bmed/14337566 bmed/14337566 muscle paresis; herniation the lesioned part ?dopt=Abstract& ?dopt=Abstract& motor weakness causing of the root; holding=npg holding=npg in lower compression on Kierman 62; extremity; root; Waxman 48-49 hyperalgesia http://www.ncbi below L1; .nlm.nih.gov/pu Brazis et al 94; bmed/19350043 http://www.ncbi ; .nlm.nih.gov/pu http://www.ncbi bmed/19350043 .nlm.nih.gov/pu bmed/18090072 L1 root tibial nerve; herniation of dorsal Dorsal cerebellum cremasteric L5/51 disc; rami/afferent Spinocerebellar (cerebellar reflex; inguinal lateral disc fibers from Tract ; cortex); region herniation dorsal root Waxman Ch 5; Waxman Ch 5; (groin/lower causing ganglion http://www.ncbi http://www.ncbi lateral regions of compression on or: right before .nlm.nih.gov/pu .nlm.nih.gov/pu abdomen); lower root; the lesioned part bmed/14337566 bmed/14337566 abdominal (L5/S1) of the root; ?dopt=Abstract& ?dopt=Abstract& paresis (internal http://www.neu Kierman 62; holding=npg holding=npg oblique, roanatomy.wisc. Waxman 48-49 transversus edu/SClinic/Radi abdominis); culo/Radiculopat Waxman 62; hy.htm; Brazis et al 95 http://www.ncbi .nlm.nih.gov/pu bmed/18090072 L2 root anterior thigh meralgia dorsal Dorsal cerebellum sensory parethetica due rami/afferent Spinocerebellar (cerebellar disturbances; to compression fibers from Tract; cortex); paresis of body of nerve; lumbar dorsal root Waxman Ch 5; Waxman Ch 5; parts:pectineus radiculopathy; ganglion http://www.ncbi http://www.ncbi (thigh adduction, lumbar disc or: right before .nlm.nih.gov/pu .nlm.nih.gov/pu flexion, and herniation into the lesioned part bmed/14337566 bmed/14337566 eversion), preganglionic of the root; ?dopt=Abstract& ?dopt=Abstract& iliopsoas (thigh region of the Kierman 62; holding=npg holding=npg flexion), nerve root; Waxman 48-49 sartorius (thigh spinal stetosis; flexion and trauma; eversion), (meralgia quadriceps (leg parethetica/lum extension), and bar)http://www. thigh adductors; ncbi.nlm.nih.gov depression of /pubmed/21294 cremasteric 431; reflex (of L2) ; Brazis et al 95; http://www.ncbi .nlm.nih.gov/pu bmed/20431433 ; L3 root lower anterior arachnoidal/dur dorsal Dorsal cerebellum thigh,medial al defect; rami/afferent Spinocerebellar (cerebellar knee; paresis in physical trauma fibers from Tract; cortex); pectineus (thigh leading to dorsal root http://www.ncbi http://www.ncbi adduction, herniation of ganglion .nlm.nih.gov/pu .nlm.nih.gov/pu flexion, and nerve root; or: right before bmed/14337566 bmed/14337566 eversion), nerve root the lesioned part ?dopt=Abstract& ?dopt=Abstract& iliopsoas (thigh entrapment in of the root; holding=npg holding=npg flexion), pseudominingoc Kierman 62; sartorius (thigh ele; lumbar Waxman 48-49 flexion and spondylolysis; eversion), (archnoidal/dura quadriceps (leg l/pseudominingo extension), and cele/lumbar thigh adductors; spondylolysis) depressed reflex http://www.joso of L2-L4 (patellar nline.org/pdf/v1 reflex) ; S1 root 8i3p367.pdf; @dorsal lateral portion of L3 level;quadricepts femoris weakness (knee); Brazis et al 95; http://www.joso nline.org/pdf/v1 8i3p367.pdf; Waxman 51 L4 root pain in lower lumbar dorsal Dorsal cerebellum back/buttock/an spondylolysis;inv rami/afferent Spinocerebellar (cerebellar terlateral ading tumors fibers from Tract; cortex); thigh/anterior involving ala of dorsal root http://www.ncbi http://www.ncbi leg; sensory sacrum ganglion .nlm.nih.gov/pu .nlm.nih.gov/pu disturbances of infringing on or: right before bmed/14337566 bmed/14337566 knee/medial leg; nerve root; the lesioned part ?dopt=Abstract& ?dopt=Abstract& paresis in tumor excision; of the root; holding=npg holding=npg muscles of neurogenic Kierman 62; leg/feet - hypertrophy Waxman 48-49 quadriceps (leg (tibialis anterior extension)sartori muscle) due to us (thigh flexion excessive and activity; eversion),tibialis Brazis et al 95; anterior (foot http://www.joso dorsiflexion and nline.org/pdf/v1 inversion); 8i3p367.pdf; depressed http://www.joso patellar reflex; nline.org/abstrac quadriceps ts/v18n3/352.ht femoris ml; weakness (knee); Brazis et al 95; http://www.joso nline.org/pdf/v1 8i3p367.pdf; http://www.joso nline.org/abstrac ts/v18n3/352.ht ml; Waxman 51 S1 root pain in lower post-osteotomy dorsal VPL Thalamus ; Primary Sensory back, buttock, surgery rami/afferent Young et al 142 Cortex lateral thigh,calf; complications; fibers from (postcentral sensory post-dissection dorsal root gyrus); disturbance of joining of S1,S2 ganglion Young et al 138- little toe, lateral leaving nerve or: right before 142; Ropper and foot, most of the roots at risk of the lesioned part Samuels Ch 9 sole of the foot; tumor invasion; of the root; paresis in clear cell Kierman 62; knee/hip/feet - sarcoma (tumor Waxman 48-49 gluteus maximus arising from S1 (hip extension), nerve root); biceps femoris http://www.joso (knee flexion), nline.org/abstrac gastrocnemius + ts/v18n3/352.ht soleus (plantar ml flexion of foot), flexor hallucis longus (plantar flexion of foot and terminal phalanx of great toe), flexor digitorum longus (plantar flexion of foot and all toes except the large toe), all of the small muscles of the foot, extensor digitorum brevis (extension of large toe + three medial toes); S1- S2 (depressed achilles reflex) ;gastrocnemius weakness; lower extremity parethesia ; Brazis et al 96; Waxman 51; http://www.ncbi .nlm.nih.gov/pu bmed/17341045 ; S2 root lower iatrogenic injury dorsal VPL Thalamus; Primary Sensory limb/bowel/blad during surgery; rami/afferent Young et al 142 Cortex der functions; post-dissection fibers from (postcentral Sensory joining of S1,S2 dorsal root gyrus); disturbances for leaving nerve ganglion Young et al 138- calf, posterior roots at risk of or: right before 142; Ropper and thigh, buttock, tumor invasion; the lesioned part Samuels Ch 9 perianal region; (surgery/tumors) of the root; Brazis et al 96; http://www.joso Kierman 62; http://www.ioso nline.org/abstrac Waxman 48-49 nline.org/abstrac ts/v18n3/352.ht ts/v18n3/352.ht ml; ml http://www.ncbi .nlm.nih.gov/pu bmed/21500136 S3 root Chronic lower invasion by dorsal VPL Thalamus ; Primary Sensory back pain; tumor; sciatica rami/afferent Young et al 142 Cortex impaired (nerve root fibers from (cerebrum) ; Sphincter compression); dorsal root Young et al 138- activity; Sensory Tarlov Cysts; ganglion 142 disturbances for Cauda Equina or: right before calf, posterior Syndrome(due the lesioned part thigh, buttock, to spinal cord of the root; perianal region; compression by Kierman 62; Brazis et al 96; drug-induced Waxman 48-49 http://www.ncbi loculation); .nlm.nih.gov/pu (Invasion by bmed/21286446 tumor)http://w ; ww.josonline.org http://www.ncbi /abstracts/v18n3 .nlm.nih.gov/pu /352.html; bmed/18034793 http://www.ncbi ; .nlm.nih.gov/pu bmed/21500136 ;http://www.ncb i.nlm.nih.gov/pu bmed/21286446

TABLE 5 Region Body Part(s) of CNS That Have That is Diminished Cause of Input Output Impaired Function Impairment Region Region Connection small center i)loss of a) Syringomyelia posterior root ventral posterior cerebral cortex lesions pain/temp b) Chiari Type ganglion axons nucleus of (primary sensory (spinothalamic sensibilities in I, II,Dandy aka dorsal root thalamus cortex or SI tract the segment Walker or: right before or: right after area) ; decussating w/lesion Malformations,t lesioned part of lesioned part of Young et al 149- fibers) (Decussating raumatic tract; tract; 150; fibers in the paraplegia, Snell 142; Kierman 292; ventral white spinal trauma, Waxman Ch 5 Waxman commisure) - spinal cord Sec III; 56,57,58 anethesia for tumors, shoulders/upper arachnoiditis,my limbs;muscle elitis; wasting in upper a) Waxman 66, limbs; ii)anterior 68; horn Kierman 77 atrophy/paresis/ b)Brazis et al areflexia; 105 i)Waxman 68; ii) Brazis et al 105 posterior/lateral cervical cord; a) Posterolateral dorsal root dorsal column primary sensory columns in thoratic cord; Column Disease or: right before nuclei (cuneate cortex (SI area); upper spinal lumbar cord (lack of B12); lesioned part of nuclei) Kierman 292; cord (dorsal degeneration; AIDS; HTLV-1; tract; or: right after Young et al 149- column-medial parethesia in tropical spastix Adams' and lesioned part of 150; lemniscus feet/hands; paraperesis; Victor's tract; http://www.ncbi pathway) aka Dorsal column cervical Neurology Ch 9 http://www.goo .nlm.nih.gov/pu dorsal cornu or dysfunction spondylosis gle.com/url?sa=t bmed/3096488 ; lateral (spine/skin (chronic disk &source=web&c http://www.ncbi cornu/horn sensation); degeneration) ; d=1&ved=OCBo .nlm.nih.gov/pu Brazis et al 106; b)sensory QFjAA&url=http bmed/8899636; Tsementzis 208 ataxia/loss of %3A%2F%2Fww http://www.goo proprioception w.biomed.cas.cz gle.com/url?sa=t and vibration %2Fphysiolres% &source=web&c sense/bilateral 2Fpdf%2F53%25 d=1&ved=OCBo spasticity/hyper 20Suppl%25201 QFjAA&url=http reflexia %2F53_5125.pdf %3A%2F%2Fww c) trauma; &ei=etY2TuP9Llr w.biomed.cas.cz a)Bravis 106; ZgQf_sdnsDA&u %2Fphysiolres% http://www.acc sg=AFQjCNG_02 2Fpdf%2F53%25 essmedicine.co zJSpiKjPhNyxivc- 20Suppl%25201 m/content.aspx VBjOpWww %2F53_5125.pdf ?alD=2319519& &ei=etY2TuP9Llr searchStr=cervic ZgQf_sdnsDA&u al+spine+diseas sg=AFQjCNG_02 e; b) Differential zJSpiKjPhNyxivc- diagnosis in VBjOpWww neurology and neurosurgery: a clinician's pocket guide By S. A. Tsementzis 208 c) http://www.ncbi .nlm.nih.gov/pu bmed/3096488 complete a) Vertebral 1) traumatic undamaged undamaged neocortex transection of Tenderness spine injuries parts of all parts of all (cingulate gyrus) spinal cord (percussion?) b) (stabbing/gunfir ascending tracts ascending tracts for sensory (transverse inhibition of e/diving into a from below the from above the ascending tracts; myelopathy) reflex anywhere shallow pool), lesion; all lesion; all Kierman 289 in the cord tumor (e.g., descending descending below the metastatic tracts from tracts from lesion; Spincter carcinoma, above the lesion below the lesion disturbance; lymphoma), ; ; back/radicular multiple Brazis et al 103 Brazis et al 103 pain c)Tactile sclerosis, stimulus above ,vascular lesion disorders,spinal d) all epidural motor/sensory hematoma functions below (usually the level of secondary to lesion; anticoagulants) a) Current or abscess, Diagnosis and paraneoplastic Treatment myelopathy, (Keith Stone) ch autoimmune 35 disorders, b)Total herniated Transverse intervertebral Lesions of the disc, and Spinal Cord parainfectious c)Adam's and or postvaccinal Victor's syndromes Neurology, 2) herpes ch44: simplex, http://www.acc influenza, essmedicine.co Epstein-Barr m/content.aspx virus), ?alD=3640629& immunisations searchStr=transv (smallpox, erse+myelitis influenza) and d) Brazis et al intoxication 103 (baclofen, penicillins, lead); ; Systemic Lupus; 3)tetraplegia (if upper cervical cord transection); paraplegia iif transection between the cervical and lumbosacral enlargements; 1. Brazis et al 103; Jeffrey et al Arch Neurol. 1993 May;50(5):532- 5. ; 2)http://ard. bmj .com/content/5 9/2/120.abstrac t 3) Kierman 76 Dorsal Root elecated touch- ALS, HIV/AIDS, peripheral dorsal medulla ganglion (aka pressure tumor nervous (posterior) horn oblongata; posterior root sensation (particularly system's cells cerebellum; ganglion) thresholds Small Cell Lung afferent/sensory or: part of the Color Atlas of (dorsal Cancer SCLC); fibers dorsal root right Textboom of column/spinoce Vitamin B6 or: part of dorsal after the Anatomy: rebellar tract intoxication (ex: Human lesioned Nervous system dysfunction due body building root right before ganglion; and Sensory to regimen,PMS the lesioned Color Atlas of Organs 50-56 demyelination);i treatment); ganglion ; Textboom of ncreased sense chemotherapy Brazis et al 89 Human of drugs especially Anatomy: pain(hyperalgesi platinum based Nervous system a)/pain due to agents (ex: and Sensory negligible Ciplatin, Organs 50-56 stimulus(allodyn carboplatin, ia) ; gait oxaliplatin etc) ; impairment, Guillian Barre, autonomic Miller Fisher system Sundrome, impairment; opthalmoplegia; (vitamin rheumatoid overdose) loss arthritis, of tendon reflex, Sjogren's progressive syndrome, sensory ataxia; Epstein-barr, bilnk reflex measles, abnormalities; varicella zoster; (ALS) (ALS) http://www.ncbi http://www.ncbi .nlm.nih.gov/pu .nlm.nih.gov/pu bmed/17929040 bmed/17929040 ; ; http://www.ncbi http://www.ncbi .nlm.nih.gov/pu .nlm.nih.gov/pu bmed/20628092 bmed/20628092 ; ; http://jn.physiol http://pn.bmj.co ogy.org/content m/content/10/6 /84/2/798.full; /326.full http://pn.bmj.co m/content/10/6 /326.full Anterior horn upper motor Spinal muscular ventral root dorsal Color Atlas of (aka anterior neurons (any Atrophies; ALS (sensory horn/columns; Textboom of column/ventral striated muscle); (degeneration of nerves); Bonica's Human horn) progressive upper motor Bonica's Management of Anatomy: weakness of the neurons/Charco Management of Pain 1497; Nervous system bulbar, is Lou Gehrig's); Pain 1497; http://onlinelibr and Sensory limb/thoracic/ progressive http://onlinelibr ary.wiley.com/d Organs 50 abdominal bulbar palsy, ary.wiley.com/d oi/10.1002/cne. musculature; progressive oi/10.1002/cne. 901790304/pdf upper motor muscular 901790304/pdf neuron atrophy (lower spasticity/paresi motor s; syndrome), Brazis et al 107- primary lateral 109; sclerosis (upper http://jnnp.bmj. motor com/content/74 syndrome),astro /9/1250.abstrac cytosis; trauma; t; stroke Tsementzis 209 (ipsilateral cerebral peduncular atrophy); non- traumatic cardiac arrest (due to spinal cord ischemia) ; Brazis et al 109, http://jnnp.bmj. com/content/74 /9/1250.abstrac t; http://www.ncbi .nlm.nih.gov/pu bmed/18024577 ; http://www.ncbi .nlm.nih.gov/pu bmed/7884198 Upper Cervical contralateral Cruciate medial cerebral Cord upper extremity Paralysis lemniscus ; hemisphere (cervicomedullar paresis and (caused by Smith et al 34- (sensory-motor y junction ipsilateral lower traumatic 35 cortex) ; injuries or extremity injuries mostly); cerebellum; malformations) paresis; lower http://www.upt Smith et al 34- extremity odate.com/cont 35; weakness ents/anatomy- http://www.ncbi (muscles and-localization- .nlm.nih.gov/pu proximal to the of-spinal-cord- bmed/19793979 lesion) ; disorders/a bstra facial/limb ct/18 hypethesia; Brazis et al 112; http://www.upt odate.com/cont ents/anatomy- and-localization- of-spinal-cord- disorders/abstra ct/18 Complete ipsilateral zone Brown-Sequard below the lesion dorsal column cerebral cortex hemisection of of cutaneous Syndrome in dorsal nuclei (cuneate (primary sensory spinal cord anethesia in the (stab/gunshot column; below nuclei); cortex or SI (dorsal column) segment of the wounds) the lesion in area) ; lesion (due to ;syringomelia spinothalamic http://www.goo undecussated (loss of tract; below the gle.com/url?sa=t Kierman 292; afferent fibers pain/temperatur lesion for any &source=web&c Young et al 149- that had already e sensation at afferent fibers; d=1&ved=0CBo 150; entered the multiple levels); Waxman 68 QFjAA&url=http http://www.ncbi spinal cord); loss spinal cord %3A%2F%2Fww .nlm.nih.gov/pu of tumor; w.biomed.cas.cz bmed/3096488 ; propioceptive/vi hematomyelia %2Fphysiolres% http://www.ncbi bratory/2-pt (hemorrhage 2Fpdf%2F53%25 .nlm.nih.gov/pu discrimination into the spinal 20Suppl%25201 bmed/8899636; sense below the cord) ; %2F53_5125.pdf http://www.goo lesion (dorsal Waxman 68 ; &ei=etY2TuP9Llr gle.com/url?sa=t column Brazis et al 105 ZgQf_sdnsDA&u &source=web&c damage); spastic sg=AFQjCNG_02 d=1&ved=OCBo weakness at zJSpiKjPhNyxivc- QFjAA&url=http level of lesion; VBjOpWww %3A%2F%2Fww loss of w.biomed.cas.cz temperature/pai %2Fphysiolres% n sensation 2Fpdf%2F53%25 below the level 20Suppl%25201 (decussated %2F53_5125.pdf spinothalamic &ei=etY2TuP9Llr tract fibers ZgQf_sdnsDA&u damage); sg=AFQjCNG_02 Waxman 68; zJSpiKjPh Nyxivc- Brazis et al 105 VBjOpWww myelin sheaths axon Multiple node of ranvier next node of of axons (PNS + degeneration; Sclerosis; Acute before the ranvier after the CNS) failure of signal inflammatory lesion; lesion in the transmission; demyelinating Waxman 25 direction of the slowing of nerve polyneuropathy action potential conduction; (AIDP), Guillain pathway; motor Barre; traumatic Waxman 25 weakness, brain injury (for paraparesis, oligodendrite paresthesia injuries)+ (numbing of subsequent skin) , diplopia degeneration of (double vision), white matter nystagmus tracts; Miller- (involuntary eye Fischer movement), Syndrome; tremor, ataxia, copper impairment of deficiency ; deep sensation, Waxman 302; and bladder DeLisa et al 899; dysfunction; Adams and blindness, Victor's tremor; Neurology Ch 36 Young et al 13; ; Waxman http://www.ncbi 24,38,302; .nlm.nih.gov/pu Adams and bmed/21669255 Victor's ; Neurology Ch 36 http://www.ncbi .nlm.nih.gov/pu bmed/21631649 ; http://www.ncbi .nlm.nih.gov/pu bmed/20685220

TABLE 6 Region of CNS That is Impaired Input Region Output Region Connection I Olfactory bipolar cells in Olfactory Bulb Olfactory Nerve olfactory (glomeruli) association epithelium or: part of cortex (frontal (cilia at surface normal nerve lobe); of epithelium in right after the Kierman 262- superior nasal lesion; 263 concha + upper Young et al 262; ⅓ of nasal http://www.ncbi septum) .nlm.nih.gov/pu or: part of bmed/21704681 normal nerve before lesion; Young et al 270; http://www.blac kwellpublishing. com/patestas/c hapters/15.pdf VIII Vestibular vestibular Vestibular nuclei Vestibulocerebel (Vestibulocochle ganglion aka or: part of lum aka ar Nerve) scarpa's normal nerve flocculonodar ganglion right after the lobe of (hair cells of lesion; cerebellum; ampullary crests Young et al 262; Kierman 335 in semicircular Kierman 335; ducts/maculae Shumway-Cook of saccule and 69 utricle) or: part of normal nerve Young et al 272; Shumway-Cook 69 VIII Cochlear otic ganglion Cochlear nuclei primary auditory (Vestibulocochle (auriculotempor (second-order area of cerebral ar Nerve) al nerve neurons) cortex (aka supplying or: part of superior paratoid gland) normal nerve temporal gyrus); or: part of right after the Kierman 326 normal nerve lesion; before lesion; Young et al 262, Schuenke et al 161 150; Young et al 272; V Trigeminal trigeminal spinal trigeminal VPM Thalamus Nerve ganglion aka nucleus (then to primary sublingual/Langl (caudal); sensory cortex); ey's ganglion ( principal Waxman ch 8 free nerve ends trigeminal in muscous nucleus mouth or: part of membrane aka normal nerve oral mucosa; right after the anterior lesion; scalp/face; free Young et al 270; nerve endings in tympanic membrane, supratentorial meninges); submandibular ganglion or: part of normal nerve before lesion; Young et al 270; http://www.ncbi .nlm.nih.gov/pu bmed/13886632 ; http://www.scie ncedirect.com/s cience/article/pi i/S03043940100 03423 VII Facial Nerve Pterygopaline Solitary Nucleus ipsilateral ganglion; or: part of cerebral cortex submandibular normal nerve (primary ganglion; right after the gustatory geniculate lesion; cortex) ; ganglion (taste Waxman 113- Kierman 131; buds in anterior 115 Young et al 193- ⅔ of mouth) 194 or: part of normal nerve (chorda tympani fibers) before lesion; Young et al 237,271; Waxman 113 IX otic ganglion Solitary Nucleus cerebral cortex Glossopharynge (auriculotempor (taste + (post central al Nerve al nerve chemoreceptor gyrus) ; supplying and Kierman paratoid gland) baroreceptor 214,215 or: part of reflexes)/Spinal normal nerve Trigeminal before lesion; Nucleus (general Young et al. 237; sensations) Waxman 257; or: part of Snell 403,405 normal nerve right after the lesion; Young et al 269; Brazis et al 325 X Vagus Nerve cardiac ganglion; Solitary nucleus cerebral cortex; bronchial (inferior Young et al 253 ganglion; ganglion)/Spinal pulmonary trigeminal ganglion; enteric nucleus(superior ganglion;intestin ) al ganglion; or: part of proximal colon normal nerve ganglion right after the or: part of lesion; normal nerve Young et al 273; before lesion; Young et al 237; http://www.ncbi .nlm.nih.gov/pu bmed/2435865; http://www.ncbi .nlm.nih.gov/pu bmed/8946336

Although in the examples above we describe and build encoders in a modular fashion with a specific set of algorithmic steps, it is evident that algorithms or devices with substantially similar input/output relationships can be built with different steps, or in a non-modular fashion, for example, by combining any two or three of the steps in to a single computational unit, such as an artificial neural network.

Given the encoders of the present disclosure, it is possible to generate data sets, without the collection of physiological data, that can be used, for example, to develop parameters for alternate spatiotemporal transformations, or to train a neural net, to produce identical or similar output using methods that are well known in the art. The explicit description of the encoders thus enables the development of prosthetics, as well as other devices, such as, but not limited to, bionics (e.g., devices providing supranormal capability) and robotics (e.g., artificial sensing systems).

The scope of the present invention is not limited by what has been specifically shown and described herein. Those skilled in the art will recognize that there are suitable alternatives to the depicted examples of materials, configurations, constructions and dimensions. Numerous references, including patents and various publications, are cited and discussed in the description of this invention. The citation and discussion of such references is provided merely to clarify the description of the present invention and is not an admission that any reference is prior art to the invention described herein. All references cited and discussed in this specification are incorporated herein by reference in their entirety.

While various inventive embodiments have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein, and each of such variations and/or modifications is deemed to be within the scope of the inventive embodiments described herein. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the inventive teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific inventive embodiments described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described and claimed. Inventive embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the inventive scope of the present disclosure. The above-described embodiments can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.

Further, it should be appreciated that a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.

Also, a computer may have one or more input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible format.

Such computers may be interconnected by one or more networks in any suitable form, including a local area network or a wide area network, such as an enterprise network, and intelligent network (IN) or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.

A computer employed to implement at least a portion of the functionality described herein may comprise a memory, one or more processing units (also referred to herein simply as “processors”), one or more communication interfaces, one or more display units, and one or more user input devices. The memory may comprise any computer-readable media, and may store computer instructions (also referred to herein as “processor-executable instructions”) for implementing the various functionalities described herein. The processing unit(s) may be used to execute the instructions. The communication interface(s) may be coupled to a wired or wireless network, bus, or other communication means and may therefore allow the computer to transmit communications to and/or receive communications from other devices. The display unit(s) may be provided, for example, to allow a user to view various information in connection with execution of the instructions. The user input device(s) may be provided, for example, to allow the user to make manual adjustments, make selections, enter data or various other information, and/or interact in any of a variety of manners with the processor during execution of the instructions.

The various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.

In this respect, various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other non-transitory medium or tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention discussed above. The computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the present invention as discussed above.

The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.

Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in any suitable form (e.g., non-transitory media). For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.

Also, various inventive concepts may be embodied as one or more methods, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

As used herein the term “light” and related terms (e.g. “optical”) are to be understood to include electromagnetic radiation both within and outside of the visible spectrum, including, for example, ultraviolet and infrared radiation.

As used herein the term “sound” and related terms (e.g. “audio”) are to be understood to include vibratory waves in any medium (e.g., gas, fluid, liquid, solid, etc.) both within and outside of the spectrum audible to humans, including, for example, ultrasonic frequencies.

All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.

Variations, modifications and other implementations of what is described herein will occur to those of ordinary skill in the art without departing from the spirit and scope of the invention. While certain embodiments of the present invention have been shown and described, it will be obvious to those skilled in the art that changes and modifications may be made without departing from the spirit and scope of the invention. The matter set forth in the foregoing description and accompanying drawings is offered by way of illustration only and not as a limitation.

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What is claimed is:
 1. A method improving or restoring neural function in a mammalian subject in need thereof, the method comprising: using an input receiver to record an input signal generated by a first set of nerve cells; using an encoder unit comprising a set of encoders to generate a set of coded outputs in response to the input signal, wherein: generating the set of coded outputs comprises transforming the input signal based on experimental neural function data of an unimpaired subject; and the experimental neural function data comprises: a first response in the unimpaired subject corresponding to the first set of nerve cells, and a second response in the unimpaired subject corresponding to a second set of nerve cells; using the coded outputs to drive an output generator; and using the output generator to activate the second set of nerve cells wherein the second set of nerve cells is separated from the first set of nerve cells by an impaired set of signaling cells; wherein the second set of nerve cells produces a response that is substantially the same as the second response in the unimpaired subject; and wherein said generating the set of coded outputs further comprises tuning the input signal based on a gain factor corresponding to: a magnitude difference between the first response in the unimpaired subject and the input signal generated by the first set of nerve cells; or a size difference between the unimpaired subject and the mammalian subject.
 2. The method of claim 1, wherein: the first set of nerve cells comprises supplementary motor area neurons; the second set of nerve cells comprises spinal motor neurons; and the impaired set of signaling cells comprises primary motor cortex neurons.
 3. The method of claim 1, wherein the transformation further comprises: generating the input signal as a time resolved series of values {right arrow over (a)} corresponding to the pattern of neural activity generated in the first set of nerve cells; and transforming the values {right arrow over (a)} to a time resolved series of output values {right arrow over (c)} by applying a transformation.
 4. The method of claim 3, wherein {right arrow over (c)} is a vector valued function, wherein each element of the vector is a value corresponding to a firing rate of a single cell or small group of cells from the second set of nerve cells.
 5. The method of claim 4, wherein {right arrow over (c)} is a vector valued function, wherein each element of the vector is a value corresponding to the total firing rate of the second set of nerve cells.
 6. The method of claim 4, wherein {right arrow over (c)} is a vector valued function, wherein each element of the vector is a value corresponding to the total firing rate of a respective subpopulation of the second set of nerve cells, and wherein the second set of nerve cells comprises motor neurons, and each subpopulation innervates a different respective muscle.
 7. The method of claim 3, wherein the transformation further comprises: a set of spatiotemporal linear filters; and a nonlinear function.
 8. The method of claim 7, wherein the spatiotemporal linear filters are parameterized by a set of K weights, and wherein K is in the range of 5-20.
 9. The method of claim 7, wherein the nonlinear function is parameterized as a cubic spline function with M knots.
 10. The method of claim 9, wherein M is in the range of 2-20.
 11. The method of claim 7, wherein the spatiotemporal linear filters operate over P time bins, each having a duration Q.
 12. The method of claim 11, wherein P is in the range of 5-20.
 13. The method of claim 12, wherein Q is in the range of 10 ms-100 ms.
 14. The method of claim 1, wherein the transformation is characterized by a set of parameters; and wherein the set of parameters corresponds to a result of fitting the transformation to the experimental neural function data obtained by: exposing the unimpaired subject to a broad range of reference stimuli; recording the first response in the unimpaired subject corresponding to the first set of nerve cells; recording the second response in the unimpaired subject corresponding to the second set of nerve cells.
 15. The method of claim 14, wherein the second response comprises the firing rate of individual nerve cells.
 16. The method of claim 14, wherein the broad range of reference stimuli comprises at least one chosen from the list consisting of: motion in an environment comprising one or more obstacles; manipulation of objects having different weights; and moving a cursor to one of several locations on a display.
 17. The method of claim 14, wherein the reference stimuli comprises an unpredictable load, and wherein the unpredictable load comprises an irregular terrain, grade, or load.
 18. A device improving or restoring neural function in a mammalian subject in need thereof, the device comprising: an input receiver configured to record an input signal generated by a first set of nerve cells; an output generator configured to activate a second set of nerve cells, wherein the second set of nerve cells is separated from the first set of nerve cells by an impaired set of signaling cells; and an encoder unit comprising a set of encoders that generate a set of coded outputs in response to the input signal, wherein: the encoder unit is configured to generate the set of coded outputs by transforming the input signal based on experimental neural function data of an unimpaired subject; the experimental neural function data comprises: a first response in the unimpaired subject corresponding to the first set of nerve cells, and a second response in the unimpaired subject corresponding to the second set of nerve cells; and the set of coded outputs control the output generator to activate the second set of nerve cells to produce a response to the input signal that is substantially the same as the second response in the unimpaired subject; and wherein the encoder unit is configured to generate the set of coded outputs by, at least in part, tuning the input signal based on a gain factor corresponding to: a magnitude difference between the first response in the unimpaired subject and the input signal generated by the first set of nerve cells; or a size difference between the unimpaired subject and the mammalian subject.
 19. The device of claim 18, wherein the output generator comprises a light outputting device configured to selectively apply light to a light-activated transducer to activate the second set of nerve cells.
 20. A non-transitory computer readable media having computer-executable instruction comprising instruction for executing steps comprising: recording an input signal generated by a first set of nerve cells; using an encoder unit comprising a set of encoders to generate a set of coded outputs in response to the input signal, wherein: generating the set of coded outputs comprises transforming the input signal based on experimental neural function data of an unimpaired subject; and the experimental neural function data comprises: a first response in the unimpaired subject corresponding to the first set of nerve cells, and a second response in the unimpaired subject corresponding to the second set of nerve cells; and using the coded outputs to control an output generator to activate the second set of nerve cells wherein the second set of nerve cells is separated from the first set of neurons by an impaired set of signaling cells; wherein the second set of nerve cells produces a response to the input signal that is substantially the same as the second response in the unimpaired subject; and wherein said generating the set of coded outputs further comprises tuning the input signal based on a gain factor corresponding to: a magnitude difference between the first response in the unimpaired subject and the input signal generated by the first set of nerve cells; or a size difference between the unimpaired subject and the mammalian subject. 